Journal Publications (For more information please send an email to epnsugan@ntu.edu.sg . Latest updates are available form Google Scholar profile)
For selected publications, we make available the codes for academic pursuits from GitHub. If you make use of these codes, please acknowledge the use of the codes, include the relevant papers in your list of references, and indicate the homepage address (http://www.ntu.edu.sg/home/epnsugan/) in your acknowledgement for the benefit of other researchers.
Updated in June 2020 (Latest updates are available form Google Scholar profile )
1. R Katuwal, PN Suganthan, L Zhang, Heterogeneous oblique random forest, Pattern
Recognition, 99, Article Number 107078, 2020. (Codes Available:
2020-PRJ-Het-Ob-RaF.zip)
2. PP Biswas, PN Suganthan, R Mallipeddi, GAJ Amaratunga, Multi-objective optimal power flow solutions using a constraint handling technique of evolutionary algorithms, Soft Computing 24 (4), 2999-3023, 2020. (Codes Available: 2020-SOCO-MOOPF)
3. R Katuwal, PN Suganthan, Stacked autoencoder based deep
random vector functional link neural network for classification,
Applied Soft Computing, 85, Article Number 105854, 2019. (Codes Available:
2019-ASOC-St-AE-Deep-RVFL).
4. D Yousri, D Allam,
MB Eteiba, PN Suganthan, Static and dynamic
photovoltaic models’ parameters identification using Chaotic Heterogeneous
Comprehensive Learning Particle Swarm Optimizer variants, Energy Conversion and
Management 182, 546-563, 2019.
5. PP Biswas, PN Suganthan, R
Mallipeddi, GAJ Amaratunga, Optimal reactive power
dispatch with uncertainties in load demand and renewable energy sources
adopting scenario-based approach, Applied Soft Computing 75, 616-632, 2019. (Codes Available:
2019-ASOC-ORPD)
6. Trinadh Pamulapati, Rammohan Mallipeddi, P. N. Suganthan, ISDE+ - An Indicator for Multi and Many-objective Optimization, IEEE Transactions on Evolutionary Computation, 23(2):346-352, 2019. (Codes Available: 2019-TEC-ISDE+)
7. G Wu, R Mallipeddi, PN Suganthan,
Ensemble strategies for population-based optimization algorithms–A survey,
Swarm and Evolutionary Computation, 44, 695-711, 2019.
8. K Gao, F Yang, MC Zhou, Q Pan, PN Suganthan, Flexible Job-Shop Rescheduling for New
Job Insertion by Using Discrete Jaya Algorithm, IEEE Transactions on
Cybernetics, 49 (5), 1944-1955, 2018.
9. K Gao, Y Zhang, R Su, F Yang, PN
Suganthan, MC Zhou, Solving traffic signal scheduling problems in heterogeneous
traffic network by using meta-heuristics, IEEE Transactions on Intelligent
Transportation Systems 20 (9), 3272-3282, 2018.
10. PP Biswas, PN Suganthan, G Wu, GAJ Amaratunga, Parameter estimation of solar cells using datasheet information with the application of an adaptive differential evolution algorithm, Renewable Energy, 2018. (Codes Available: 2018-RenEnergy-Param-Solar)
11. A Majumder,
D Laha, PN Suganthan, A hybrid cuckoo search algorithm in parallel
batch processing machines with unequal job ready times, Computers
& Industrial Engineering, 124, 65-76, 2018.
12. R Katuwal, PN Suganthan, L
Zhang, An Ensemble of Decision Trees with Random Vector
Functional Link Networks for Multi-Class Classification, Applied
Soft Computing, 2018 (Codes Available:
2018-ASOC-RVFL-RF)
13. PN Suganthan, On non-iterative learning algorithms with closed-form
solution, Applied Soft Computing, 70,
1078-1082, 2018
14. PP Biswas, PN Suganthan,
BY Qu, GAJ Amaratunga, Multiobjective economic-environmental
power dispatch with stochastic wind-solar-small hydro power, Energy
150, 1039-1057, 2018. (Codes Available:
2018-Energy-MOEED-WSH)
15. B Huang, W Chen, X Wu, CL
Lin, PN Suganthan, High-quality face image generated
with conditional boundary equilibrium generative adversarial networks,
Pattern Recognition Letters 111, 72-79, 2018.
16. MZ Ali, NH Awad, RG
Reynolds, PN Suganthan, A balanced fuzzy Cultural Algorithm
with a modified Levy flight search for real parameter optimization,
Information Sciences 447, 12-35, 2018
17. NH Awad, MZ Ali, R
Mallipeddi, PN Suganthan, An improved differential evolution
algorithm using efficient adapted surrogate model for numerical optimization,
Information Sciences 451, 326-347, 2018.
(Codes Available:
2018-INS-Surrogate)
18. BY Qu, YS Zhu, YC Jiao, MY
Wu, PN Suganthan, JJ Liang, “A Survey on Multi-objective Evolutionary Algorithms for the Solution
of the Environmental/Economic Dispatch Problems,” Swarm and Evolutionary Computation, 38, 1-11, 2018.
19. PP Biswas, PN Suganthan,
GAJ Amaratunga, Decomposition based multi-objective
evolutionary algorithm for windfarm layout optimization, Renewable
Energy 115, 326-337, 2018. (Codes Available:
2018-RENE-Windfarm)
20. NH Awad, MZ Ali, PN
Suganthan, Ensemble of Parameters in a Sinusoidal
Differential Evolution with Niching-based population Reduction, Swarm and Evolutionary
Computation, 39, 141-156, 2018. (Codes Available:
2018-SWEVO-EP-SIN)
21. N Lynn, MZ Ali, PN
Suganthan, Population topologies for particle swarm optimization
and differential evolution, Swarm and Evolutionary Computation,
2017, 39, 24-35, 2018.
22. PP Biswas, PN Suganthan, R
Mallipeddi, GAJ Amaratunga, Optimal power flow solutions using differential evolution
algorithm integrated with effective constraint handling techniques
Engineering Applications of Artificial Intelligence 68, 81-100, 2018. (Codes Available:
2018-EAAI-OPF-DE-CH)
23. G Wu, X Shen, H Li, H
Chen, A Lin, PN Suganthan, Ensemble of differential
evolution variants, Information Sciences 423, 172-186, 2018. (Codes Available:
2018-INS-EDEV)
24. X Qiu,
PN Suganthan, GAJ Amaratunga, Ensemble
Incremental Learning Random Vector Functional Link Network for Short-term
Electric Load Forecasting, Knowledge-Based Systems, 145, 182-196,
2018. (Codes Available:
2018-KBS-RVFL-Forecast , i.e. Online Sequential RVFL)
25. AW Mohamed, PN Suganthan,
Real-parameter unconstrained optimization based on enhanced fitness-adaptive
differential evolution algorithm with novel mutation, Soft
Computing, 22 (10), 3215-3235
(Codes Available:
2018-Soft-Comp) 2018.
26. MZ Ali, NH Awad, PN Suganthan, AM Shatnawi,
RG Reynolds, An
improved Class of Real-Coded Genetic Algorithms for Numerical Optimization, Neurocomputing, 275, 155-166, 2018. (Codes Available:
2018-Neucom-GA-SPS)
27. L. Zhang, P. N. Suganthan, Benchmarking Ensemble Classifiers with Novel Co-trained Kernel Ridge Regression and Random Vector Functional Link Ensembles, IEEE Computational Intelligence Magazine, 12 (4), 61-72. Nov 2017. (Codes Available: 2017-CIM-codes)
28. MZ Ali, NH Awad, PN Suganthan, RG Reynolds, “An Adaptive Multipopulation Differential Evolution with Dynamic Population Reduction, IEEE Transactions on Cybernetics, 47 (9), 2768-2779, 2017.
29. PP Biswas, PN Suganthan, GAJ Amaratunga,
Minimizing harmonic distortion in power system with
optimal design of hybrid active power filter using differential evolution,
Applied Soft Computing 61, 486-496, 2017. (Codes Available:
2017-ASOC-Filter)
30. BY Qu, JJ Liang, YS Zhu, PN Suganthan, “Solving dynamic economic emission dispatch problem considering wind power by multi-objective differential evolution with ensemble of selection method,” Natural Computing, 2017, pp. 1-9.
31. X Qiu, L
Zhang, PN Suganthan, GAJ Amaratunga, Oblique
random forest ensemble via Least Square Estimation for time series forecasting,
Information Sciences 420, 249-262, 2017 (Codes Available:
2017-INS-ObRF-Forecast)
32. L. Zhang, P. N. Suganthan, "Visual Tracking with Convolutional Random Vector Functional Link Network," IEEE Trans on Cybernetics, 47 (10), 3243-3253, 2017. Codes Videos Available
33. G Wu, W Pedrycz,
PN Suganthan, H Li, Using Variable Reduction Strategy to Accelerate
Evolutionary Optimization, Applied
Soft Computing, 61, 283-293, 2017.
34. PP
Biswas, R Mallipeddi, PN Suganthan, GAJ Amaratunga, A multiobjective approach
for optimal placement and sizing of distributed generators and capacitors in
distribution network, Applied
Soft Computing, 60, 268-280, 2017. (Codes Available:
2017-ASOC-MO-RDN)
35. S. Sundar, P. N. Suganthan, C. T. Jin, C. T. Xiang, C. C. Soon, "A hybrid artificial bee colony algorithm for the job-shop scheduling problem with no-wait constraint," Soft Computing, doi: 10.1007/s00500-015-1852-9, 21(5) 1193-1202, 2017.
36.
BY Qu, Q Zhou, JM Xiao, J. J. Liang and P. N.
Suganthan, “Large Scale Portfolio Optimization Using Multi-objective
Evolutionary Algorithms and Pre-selection Methods", Mathematical Problems
in Engineering, Article Number:
37. L Zhang, PN Suganthan, Robust
visual tracking via co-trained Kernelized correlation filters, Pattern
Recognition 69, 82-93, 2017 (Codes Available).
38. X. H. Qiu, Y.
Ren, P. N. Suganthan, G. Amaratunga, “Empirical Mode Decomposition based
Ensemble Deep Learning for Load Demand Time Series Forecasting,” Applied Soft
Computing, 54, 246-255, 2017 (Codes Available:
2017-ASOC-EMD-DBN).
39. PP
Biswas, PN Suganthan, GAJ Amaratunga, Optimal power flow
solutions incorporating stochastic wind and solar power, Energy
Conversion and Management 148, 1194-1207, 2017. (Codes Available:
2017-ECM-OPF-WS)
40. N. Lynn, P. N. Suganthan, “Ensemble particle swarm optimizer,” Applied Soft Computing, 55, 533-548, 2017. (Codes Available: 2017-ASOC-EPSO)
41. A. Rajasekhar, N. Lynn, S. Das, and P. N. Suganthan, "Computing with the Collective Intelligence of Honey Bees – A Survey," Swarm and Evolutionary Computation, DoI: 10.1016/j.swevo.2016.06.001, Feb 2017. (supplementary file available here)
42. MF Tasgetiren, D Kizilay, QK Pan, PN Suganthan, “Iterated greedy algorithms for the blocking flowshop scheduling problem with makespan criterion,” Computers & Operations Research 77, 111-126, 2017.
43. NH Awad, MZ Ali, PN Suganthan, RG Reynolds, “CADE: A hybridization of Cultural Algorithm and Differential Evolution for numerical optimization,” Information Sciences 378, 215-241, 2017.
44. T Jayabarathi, T Raghunathan, BR Adarsh, PN Suganthan, “Economic dispatch using hybrid grey wolf optimizer,” Energy 111, 630-641, 2016.
45. KZ Gao, PN Suganthan, QK Pan, MF Tasgetiren, A Sadollah, “Artificial bee colony algorithm for scheduling and rescheduling fuzzy flexible job shop problem with new job insertion,” Knowledge-Based Systems 109, 1-16, 2016.
46. MZ Ali, NH Awad, PN Suganthan, RG Reynolds, “A modified cultural algorithm with a balanced performance for the differential evolution frameworks,” Knowledge-Based Systems 111, 73-86, 2016
47. NH Awad, MZ Ali, PN Suganthan, E Jaser, “A decremental stochastic fractal differential evolution for global numerical optimization,” Information Sciences 372, 470-491, 2016.
48. KZ Gao, PN Suganthan, QK Pan, TJ Chua, CS Chong, TX Cai, “An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time,” Expert Systems with Applications 65, 52-67, 2016.
49. BY Qu, JJ Liang, YS Zhu, ZY Wang, PN Suganthan, “Economic emission dispatch problems with stochastic wind power using summation based multi-objective evolutionary algorithm,” Information Sciences 351, 48-66, 2016.
50. I Fister, PN Suganthan, I Fister Jr, SM Kamal, FM Al-Marzouki, M Perc, “Artificial neural network regression as a local search heuristic for ensemble strategies in differential evolution,” Nonlinear Dynamics 84 (2), 895-914, 2016
51. S. Das, S. S. Mullick, P. N. Suganthan, "Recent Advances in Differential Evolution - An Updated Survey," Swarm and Evolutionary Computation, Vol. 27, pp. 1-30, April, 2016.
52. L. Zhang, P. N. Suganthan, "A Survey of Randomized Algorithms for Training Neural Networks," Information Sciences, DoI: 10.1016/j.ins.2016.01.039, Volumes 364–365, pp.146–155, Oct, 2016.
53. Y. Ren, L. Zhang, and P. N. Suganthan, "Ensemble Classification and Regression – Recent Developments, Applications and Future Directions," IEEE Computational Intelligence Magazine, DOI: 10.1109/MCI.2015.2471235, Feb 2016.
54. Y. Ren, P. N. Suganthan, N. Srikanth, G. Amaratunga, "Random Vector Functional Link Network for Short-term Electricity Load Demand Forecasting", Information Sciences, Volumes 367–368, pp 1078–1093, Nov. 2016. (Codes Available: 2016-INS-RVFL-Time-Series) Also from: https://github.com/ron1818/PhD_code/ (RVFL Matlab Codes for Time Series: 2016-RVFL withQScaling.m)
55. G. H. Wu, R. Mallipeddi, P. N. Suganthan, R. Wang, H. K. Chen, "Differential Evolution with Multi-Population Based Ensemble of Mutation Strategies," Information Sciences, DOI: 10.1016/j.ins.2015.09.009, pp. 329-345, 2016. (Codes Available: 2016-INS-MEPDE)
56. M. Z. Ali, N. H Awad, P. N. Suganthan, R. M. Duwairi, R. G. Reynolds, "A Novel Hybrid Cultural algorithms framework with Trajectory-based Search for Global Numerical Optimization," Information Sciences, Volumes 334–335, 20 March 2016, pp. 219-249.
57. M. Z. Ali, P. N. Suganthan, R. G. Reynolds, and A. F. Al-Badarneh, "Leveraged Neighborhood-Restructuring in Cultural Algorithms for Solving Real-World Problems," IEEE Trans on Evol. Comp., Volume: 20, Issue: 2, pp. 218 – 231, April 2016, DoI: 10.1109/TEVC.2015.2450018
58. L. Zhang, P. N. Suganthan, "A Comprehensive Evaluation of Random Vector Functional Link Networks," Information Sciences, DOI: 10.1016/j.ins.2015.09.025, Volumes 367–368, pp. 1094–1105, Nov 2016. (Codes Available: 2016-RVFL-Comp-Eval-Classification)
59. M. Z. Ali, N. H. Awad, P. N. Suganthan, "Multi-population differential evolution with balanced ensemble of mutation strategies for large-scale global optimization," Applied Soft Computing 33, 304-327, 2015.
60. G. Wu, W. Pedrycz, P. N. Suganthan, R. Mallipeddi, "A variable reduction strategy for evolutionary algorithms handling equality constraints," Vol. 37, Dec. 2015, pp 774–786, Applied Soft Computing. (Codes Available: 2015-ASOC-Constr-Variable-Reduce)
61. N. Lynn, P. N. Suganthan, "Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation," Swarm and Evolutionary Computation 24, pp. 11-24, 2015. (Codes Available: 2015-SWEVO-HCLPSO)
62. Y. Ren, P. N. Suganthan, N. Srikanth, "Ensemble methods for wind and solar power forecasting—A state-of-the-art review," Renewable and Sustainable Energy Reviews 50, 82-91, 2015.
63. K. Z. Gao, P. N. Suganthan, M. F. Tasgetiren, Q. K. Pan, Q. Q. Sun, "Effective Ensembles of Heuristics for Scheduling Flexible Job Shop Problem with New Job Insertion," Vol. 90, Dec. 2015, pp. 107–117, Computers & Industrial Engineering. (Codes Available: 2015-CAIE-1 & 2015-CAIE-2)
64. K. Z. Gao, P. N. Suganthan, T. J. Chua, C. S. Chong, T. X. Cai, Q. K. Pan, "A two-stage artificial bee colony algorithm scheduling flexible job-shop scheduling problem with new job insertion," Expert Systems with Applications 42 (21), 7652-7663, 2015. (Codes Available: 2015-ESWA-GKZ))
65. B. Y. Qu, J. J. Liang, Z. Y. Wang, Q. Chen, P. N. Suganthan, "Novel benchmark functions for continuous multimodal optimization with comparative results," Swarm and Evolutionary Computation, DoI: 10.1016/j.swevo.2015.07.003 Volume 26, February 2016, Pages 23-34. (Codes Available)
66. I Fister, K Ljubič, PN Suganthan,
M Perc, "Computational intelligence in sports: Challenges and
opportunities within a new research domain," Applied
Mathematics and Computation 262, 178-186. 2015.
67. Y. Ren, P. N. Suganthan, N. Srikanth, “A Novel Empirical Mode
Decomposition With Support Vector Regression for Wind Speed Forecasting,” IEEE, 2014 Trans on Neural Networks and Learning Systems, in press
2015. Trans on Neural Networks and Learning
Systems, DoI: 10.1109/TNNLS.2014.2351391
in press 2015. (Codes Available:
2015-TNNLS-TSTE)
68. L. Zhang, P. N. Suganthan, “Oblique Decision Tree Ensemble via Multisurface Proximal Support Vector Machine,” IEEE Trans on Cybernetics, DoI: 10.1109/TCYB.2014.2366468 , Vol. 45, No. 10, pp. 2165-2176, Oct 2015 (supplementary file available here). (Codes Available: 2015-TCyb-Oblique-RF or from HERE)
69. S. Hui, P. N. Suganthan, “Ensemble and Arithmetic Recombination-Based Speciation Differential Evolution for Multimodal Optimization,” IEEE Trans on Cybernetics, DoI: 10.1109/TCYB.2015.2394466 , 2015.
70.
K. Z. Gao, P. N. Suganthan, Q. K. Pan, M. F.
Tasgetiren, “An effective discrete harmony search algorithm for flexible job
shop scheduling problem with fuzzy processing time,” Int. J. of Production
Research, Vol. 53, Issue 19, pp. 5896-5911, 2015. (Codes
Available: 2015-IJPR-GKZ))
71. Y. Ren, P. N. Suganthan, N. Srikanth, “A Comparative Study of Empirical Mode Decomposition-Based Short-Term Wind Speed Forecasting Methods,” IEEE Transactions on Sustainable Energy, 6 (1), 236-244, 2015. (Codes Available: 2015-TNNLS-TSTE)
72.
D. Laha, Y. Ren, P. N. Suganthan, “Modelling of
steelmaking process with effective machine learning techniques,” Expert Systems
with Applications 42 (10), 4687-4696, 2015. (Codes Available: 2015-ESWA)
73. Y Ren, PN Suganthan, “Empirical Mode Decomposition-k Nearest Neighbor Models for Wind Speed Forecasting,” Journal of Power and Energy Engineering 2 (04), 176, 2014. (Codes Available: 2014-JPEE-Ren-Ye)
74. KZ Gao, PN Suganthan, QK Pan, TJ Chua, TX Cai, CS Chong , “Pareto-based grouping discrete harmony search algorithm for multi-objective flexible job shop scheduling,” Information Sciences 289, 76-90, 2014.
75. R. Mallipeddi, P. N. Suganthan, “Unit commitment–a survey and comparison of conventional and nature inspired algorithms,” Int. Journal of Bio-Inspired Computation 6 (2), 71-90, 2014.
76. KZ Gao, PN
Suganthan, QK Pan, TJ Chua, TX Cai, CS Chong, “Discrete harmony search
algorithm for flexible job shop scheduling problem with multiple objectives,”
Journal of Intelligent Manufacturing, DoI: 2014.
77.
J Li, Q Pan, K Mao, PN Suganthan, “Solving the steelmaking
casting problem using an effective fruit fly optimisation algorithm,”
Knowledge-Based Systems 72, 28-36, 2014.
78. L. Zhang, P N Suganthan, “Random Forests with Ensemble of Feature Spaces,” Pattern Recognition, 47 (10), 3429-3437, 2014. (Codes Available: 2015-TCyb-Oblique-RF , i.e. a single package for 2 papers)
79. G Kandavanam, R Mallipeddi, D Botvich, S Balasubramaniam, P. N. Suganthan, “Achieving High Robustness and Performance in QoS-aware Route Planning for IPTV Networks,” Information Sciences, 269, 217-237, 2014. (Codes Available: 2014-INS-QoS-Route)
80. L Wang, C Fang, PN Suganthan, M Liu, “Solving system-level synthesis problem by a multi-objective estimation of distribution algorithm,” Expert Systems with Applications 41 (5), 2496-2513, 2014.
81. J Derrac, S García, S Hui, PN Suganthan, F Herrera, “Analyzing convergence performance of evolutionary algorithms: A statistical approach,” Information Sciences 289, 41-58, 2014.
82. M F. Tasgetiren, Q. K. Pan, P.N. Suganthan, A. Oner, “A Discrete Artificial Bee Colony Algorithm for the No-Idle Permutation Flow shop Scheduling Problem with the Total Tardiness Criterion,” Applied Mathematical Modeling, Vol. 37, Issues 10–11, 1 June 2013, pp. 6758–6779.
83. S. Z. Zhao, P. N. Suganthan, “Empirical investigations into the exponential crossover of differential evolution,” Swarm and Evolutionary Computation, DOI: /j.swevo.2012.09.004, Feb 2013, pp. 27-36.
84. K Gao, Q Pan, P N Suganthan, J Li, “Effective heuristics for the no-wait flow shop scheduling problem with total flow time minimization,” Int. J. of Advanced Manufacturing Technology, .
85. M F. Tasgetiren, Q. K. Pan, P.N. Suganthan, O. Buyukdagli, “A Variable Iterated Greedy Algorithm with Differential Evolution for the No-Idle Permutation Flow shop Scheduling Problem”, Computers & Operations Research, Vol. 40, Issue 7, July 2013, pp. 1729–1743.
86. S. Sengupta, S. Das, Md. Nasir, P. N. Suganthan, "Risk Minimization in Biometric Sensor Networks – An Evolutionary Multi-objective Optimization Approach", Soft Computing, (Codes Available: 2013-Soft-Comp-biometric)
87. B-Y Qu, P. N. Suganthan, S. Das, "A Distance-Based Locally Informed Particle Swarm Model for Multi-modal Optimization,", IEEE Trans on Evolutionary Computation, DOI: 10.1109/TEVC.2012.2203138. (Supplementary file), 2013. (Codes Available: 2013-TEC-LIPS)
88.
B. Y. Qu, P. N. Suganthan
and J. J. Liang, “
89. K Z Gao,
P N Suganthan, T J Chua, “Discrete Harmony Search Algorithm for the
Disassembly Scheduling Remanufacturing Engineering, Applied
Mechanics and Materials, 236, 169-174, 2012
90. Md.
Nasir, D. Maity, S. Das, S. Sengupta, U. Haldar, P. N. Suganthan, "A
Dynamic Neighborhood Learning based Particle Swarm
Optimizer for Global Numerical Optimization," Information Sciences, Vol. 209, pp. 16–36, Nov. 2012. (Codes Available:
2012-INS-dnlpso)
91. R.
Mallipeddi, P. N. Suganthan, "Efficient Constraint Handling for Optimal
Reactive Power Dispatch Problem," Swarm
and Evolutionary Computation, Vol. 5,
pp. 28–36, Aug. 2012. (Codes Available:
2012-SWEVO-Cnostr-Handl-4-power)
92. S. Z. Zhao, P.
N. Suganthan, Q. Zhang, "Decomposition Based
Multiobjective Evolutionary Algorithm with an Ensemble of Neighborhood
Sizes", IEEE Trans. on
Evolutionary Computation, Vol. 16, No. 3, pp. 442-446, June 2012. (Codes Available:
2012-TEC-Ens-MOEAD)
93. S. Ghosh, S.
Das, S. Roy, Sk. Minhazul Islam, and P. N. Suganthan "A Differential
Covariance Matrix Adaptation Evolutionary Algorithm for Real Parameter
Optimization", Information Sciences, Vol. 182, No. 1,
pp 199-219 Jan. 2012. (Codes Available:
2012-INS-CMA-DE)
94. B-Y
Qu, P N Suganthan, J J Liang, "Differential
Evolution with Neighborhood Mutation for Multimodal
Optimization," IEEE Trans on Evolutionary Computation, DOI: 10.1109/TEVC.2011.2161873. (Supplementary file), Oct 2012. (Codes
Available: 2012-TEC-DE-niching)
95. Sk.
Minhazul Islam, S. Das, S. Ghosh, S. Roy, and P. N. Suganthan, "An Adaptive Differential Evolution Algorithm with Novel Mutation
and Crossover Strategies for Global Numerical Optimization", IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 42, No. 2, pp. 482-500,
2012.
96. R. Mallipeddi, J. P. Lie, S. G. Razul, P. N. Suganthan, S. C. M.
See, "Robust
Adaptive Beamforming Based on Covariance Matrix Reconstruction for Look
Direction Mismatch", PIER Letters, Vol. 25, pp. 37-46, 2011. (Codes Available:
2011-PIER3-Codes, also refer to PIER2 & PIER1)
97. R. Mallipeddi,
J. P. Lie, P. N. Suganthan, S. G. Razul, S. C. M. See, "A Differential
Evolution Approach for Robust Adaptive Beamforming Based on Joint Estimation of
Look Direction and Array Geometry", PIER -119, pp. 381-394,
2011 (Codes Available:
2011-PIER2-Codes, also refer to PIER1 & PIER3)
98. S. Das, S. Maity,
B-Y Qu, P.N. Suganthan, "Real-parameter evolutionary multimodal
optimization — A survey of the state-of-the-art", Vol. 1, No. 2, pp.
71-88, Swarm and
Evolutionary Computation, June 2011, DOI: 10.1016/j.swevo.2011.05.005.
99. S. Sivananaithaperumal,
S. Miruna Joe Amali, S.
Baskar, P. N. Suganthan, "Constrained self-adaptive differential evolution
based design of robust optimal fixed structure controller", Engineering Applications of Artificial Intelligence, Vol. 24, No. 6, pp. 1084-1093, SEP 2011. (Codes Available:
2011-EAAI-SaDE-robust-controller)
100. M. F. Tasgetiren, Q-K Pan, P. N. Suganthan and A. H-L Chen, "A Discrete Artificial Bee Colony Algorithm for the Total Flowtime Minimization in Permutation Flow Shops", Information Sciences, Vol. 181, No. 16, pp. 3459-3475, 2011.
101. S. Z. Zhao, M. I. Willjuice, S. Baskar, P. N. Suganthan, "Multi-objective Robust PID Controller Tuning using Two Lbests Multi-objective Particle Swarm Optimization", Information Sciences, Vol. 181, No.16, pp. 3323-3335, AUG 2011. (Codes Available: 2011-Ins-PID control)
102.
R. Mallipeddi, J.
P. Lie, P. N. Suganthan, S. G. Razul,
S. C. M. See, "Near Optimal Robust Adaptive Beamforming Approach Based on
Evolutionary Algorithm", PIER B
29, pp. 157-174, 2011. (Codes Available:
2011-PIER1-Codes, also refer to PIER2 & PIER3)
103. A. Zhou, B-Y. Qu, H. Li, S-Z. Zhao, P. N. Suganthan, Q. Zhang, "Multiobjective Evolutionary Algorithms: A Survey of the State-of-the-art", Swarm and Evolutionary Computation, Vol. 1, No. 1, pp. 32-49, Mar 2011, DOI: 10.1016/j.swevo.2011.03.001.
104. K. K. Kandaswamy, K-C. Chou, T. Martinetz, S. Möller, P. N. Suganthan, S. Sridharan, P. Ganesan, "AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties," J. of Theoretical Biology, Vol. 270, No. 1, pp. 56-62, FEB. 2011.
105. S. Z. Zhao, P. N. Suganthan, Q.-K. Pan, M. F. Tasgetiren, "Dynamic Multi-Swarm Particle Swarm Optimizer with Harmony Search", Expert Systems with Applications, Vol. 38, No. 4, pp. 3735-3742, APR 2011. (Codes Available: 2011-ESWA-DMS-PSO+HS)
106. Q-K Pan , P. N. Suganthan , J. J. Liang, M. F. Tasgetiren, A Local-best harmony search algorithm with dynamic sub-harmony memories for lot-streaming flow shop scheduling problem", Expert Systems with Applications, Vol. 38, No. 4, April 2011, pp. 3252-3259.
107.
G. G. Roy, S. Das, P. Chakraborty, and
P. N. Suganthan , "Design of Non-Uniform Circular Antenna Arrays using a
Modified Invasive Weed optimization Algorithm," IEEE Trans. on Antenna
and Propagation, Vol. 59, No. 1, Jan. 2011, pp. 110 - 118. (Codes Available:
2011-TAP-Circular-IWO)
108.
S. Z. Zhao and P. N. Suganthan, S. Das,
"Self-adaptive Differential Evolution with Multi-trajectory Search for
Large Scale Optimization," Soft Computing, DOI: 10.1007/s00500-010-0645-4.
(Codes Available:
2011-Soft-Comp-SaDE-MMTS)
109.
K. K. Kandaswamy, G. Pugalenthi, S. Möller, E. Hartmann, K. Kalies, P. N.
Suganthan, T. Martinetz, "Prediction of apoptosis protein locations with
Genetic Algorithms and Support-Vector-Machines", Protein Peptide Letters, Vol. 17 No. 12 pp. 1473-1479 DEC 2010.
110.
G. Pugalenthi,
111. Q-K. Pan, P. N. Suganthan, L. Wang, L. Gao, R. Mallipeddi, "A Differential Evolution Algorithm with Self-Adapting Strategy and Control Parameters," Computers and Operations Research, Vol. 38 No. 1 pp. 394-408 JAN 2011.
112. M. F. Tasgetiren, Q. K. Pan, P. N. Suganthan, T. J. Chua, "A Differential Evolution Algorithm for the No-Idle Flowshop Scheduling Problem with Total Tardiness Criterion", International Journal of Production Research, Vol. 49, Issue 16, pp. 5033-5050, 2011.
113.
J-Q Li, Q-K Pan, P. N. Suganthan,
T. J. Chua, "A hybrid tabu search algorithm with an efficient neighborhood
structure for the flexible job shop scheduling problem", Int.
J. of Advanced Manufacturing Technology, Vol. 52, No.
5-8, pp. 683-697, FEB
2011.
114.
S.
Das, P. N. Suganthan, C. A. Coello Coello, "Guest
Editorial Special Issue on Differential Evolution", Vol. 15,
No. 1, pp. 1-3, DOI: 10.1109/TEVC.2011.2108970.
115.
S. Das and P. N.
Suganthan, "Differential
Evolution: A Survey of the State-of-the-art", IEEE Trans.
on Evolutionary Computation, Vol. 15, No. 1, pp. 4-31, Feb. 2011,
DOI: 10.1109/TEVC.2010.2059031. (supplementary
reference list cited as [Sxxx] is available).
116.
B. Y. Qu, P. N. Suganthan, "Constrained Multi-Objective
Optimization Algorithm with Ensemble of Constraint Handling Methods", Engineering Optimization, Vol. 43,
No. 4, p. 403, 2011 (Codes
Available: 2011-Eng-Opt-Constr-MODE).
117.
S. Z. Zhao and P. N.
Suganthan, “Two-lbests Based Multi-objective Particle
Swarm Optimizer”, Engineering Optimization, DOI: 10.1080/03052151003686716, Vol. 43
No. 1 pp. 1-17, 2011 (Codes Available: 2011-Eng-Opt-2LB-MOPSO).
118.
R. Mallipeddi, P. N. Suganthan, Q. K.
Pan, M. F. Tasgetiren, "Differential evolution algorithm with ensemble of
parameters and mutation strategies" Applied Soft Computing, DOI:10.1016/j.asoc.2010.04.024,
Vol. 11 No. 2 pp. 1679-1696, MAR 2011 (Codes Available:
2011-EPSDE-ASOC).
119.
B. Y. Qu, P. N. Suganthan,
“Multi-Objective Evolutionary Algorithms based on the Summation of Normalized
Objectives and Diversified Selection”, Information
Sciences, Vol. 180, No. 17, 1 Sept. 2010, pp. 3170-3181, Doi:10.1016/j.ins.2010.05.013 (Codes Available:
2010-Inf-Sci-Fast-Sort-MODE).
120.
S. Pal, S. Das, A. Basak, and P. N. Suganthan, "Synthesis of difference
patterns for monopulse antennas with optimal
combination of array-size and number of subarrays - A multiobjective
optimization approach", Progress
in Electromagnetics Research, PIER B, Vol. 21,
pp. 257-280, 2010.
121.
S. Pal, B. Y. Qu, S.
Das, and P. N. Suganthan, "Optimal Synthesis of Linear Antenna Arrays with
Multi-objective Differential Evolution", Progress in Electromagnetics Research, PIER B, Vol. 21, pp. 87-111, 2010.
122.
A. Anand, G. Pugalenthi, G. B. Fogel, P. N. Suganthan, "An approach for classification of highly imbalanced
biological data using weighting and undersampling",
Amino Acids, DOI:
10.1007/s00726-010-0595-2, Vol. 39 No. 5 pp. 1385-1391, Nov 2010 (Codes Available: 2010-Amino-A-Imbalanced-Classification).
123.
E. L. Yu, P. N.
Suganthan, "Ensemble of niching algorithms", Information Sciences, Vol. 180, No. 15,
pp. 2815-2833, Aug. 2010, DOI: 10.1016/j.ins.2010.04.008.
124.
B. Y. Qu, P. N. Suganthan, “Multi-Objective
Differential Evolution with Diversity Enhancement”, Journal of Zhejiang University-SCIENCE A, Vol. 11 No. 7 pp. 538-543 JUL 2010.
125.
G. Pugalenthi, K. K. Kandaswamy, P.
N. Suganthan, G. Archunan and R. Sowdhamini,
"Identification
of functionally diverse lipocalin proteins from sequence information using
support vector machine", Amino
Acids,
Vol. 39 No. 3 pp.
777-783, Aug, 2010, DOI: 10.1007/s00726-010-0520-8.
126.
Q-K Pan, P. N.
Suganthan, M. F. Tasgetiren, J. J. Liang, "A Self-Adaptive Global Best Harmony Search Algorithm
for Continuous Optimization Problems ", Applied Mathematics and
Computation, Vol. 216, No. 3, April 2010, pp. 830-848, DOI: 10.1016/j.amc.2010.01.088 (Codes Available:
2010-AMC-GBest-HS).
127.
R. Mallipeddi, S.
Mallipeddi, P. N. Suganthan, “Ensemble strategies with adaptive
evolutionary programming”, Information
Sciences, vol. 180, no. 9, May 2010, pp. 1571-1581, DOI:10.1016/j.ins.2010.01.007 (Codes Available:
2010-INS-Ens-Str-EP).
128.
Q-K Pan, M. F.
Tasgetiren, P. N. Suganthan; T. J. Chua, "A Discrete Artificial Bee Colony Algorithm for the
Lot-streaming Flow Shop Scheduling Problem", Information Sciences, Vol. 181 No. 12, pp.
2455–2468, 2011.
129.
R. Mallipeddi, P. N. Suganthan, “Ensemble
of Constraint Handling Techniques”, IEEE
Trans. on Evolutionary Computation, Vol. 14,
No. 4, pp. 561 - 579 , Aug. 2010, DOI: 10.1109/TEVC.2009.2033582 (Codes Available:
2010-TEC-Ens-Con-EP-DE).
130.
A. Anand, G
Pugalenthi, G B Fogel, P.N.
Suganthan, "Identification and Analysis of Transcription Factor
Family-specific Features Derived from DNA and Protein
Information", Pattern Recognition Letters, DOI: 10.1016/j.patrec.2009.10.008, Vol. 31 No. 14,
pp. 2097-2100, OCT
2010.
131.
L. Q. Song, M. H. Lim and P. N. Suganthan, “Ensemble of Optimization Algorithms for Solving Quadratic
Assignment Problems”, Int. J. of
Innovative Computing, Information and Control, Oct. 2010.
132.
K. Krishna Kumar, G. Pugalenthi, P. N. Suganthan, R. Gangal,
"SVMCRYS: An SVM approach for the prediction of protein crystallization
propensity from protein", Protein Peptide Letters, Vol.
17, No. 4, pp. 423-430, April 2010.
133.
S. Z. Zhao and P. N. Suganthan, “Multi-objective Evolutionary Algorithm
with Ensemble of External Archives”, Int.
J. of Innovative Computing, Information and Control, Vol. 6, No. 1, pp
1713-1726, April 2010.
134.
L. Wang, Q-K. Pan, P. N. Suganthan, W. H. Wang,
Y. M. Wang, “A novel hybrid discrete differential
evolution algorithm for blocking flow shop scheduling problems”, Computers & operations Research, Vol. 37, No. 3, pp. 509-520, Mar. 2010. DOI:10.1016/j.cor.2008.12.004.
135.
Q.-K. Pan,
P. N. Suganthan, J. J. Liang , M. F. Tasgetiren, "A Local-Best
Harmony Search Algorithm with Dynamic Subpopulations", Engineering
Optimization, Vol. 42, Issue 2, February 2010, pp. 101 - 117. (Codes Available:
2010-EngOpt-LBest-HS)
136.
Q. K. Pan, P. N. Suganthan, T. J. Chua, T. X. Cai, "Solving
manpower scheduling problem in manufacturing using mixed-integer programming
with a two-stage heuristic algorithm", Int.
J. of Advanced Manufacturing Technology, Vol. 46, No. 9-12 pp. 1229-1237, Feb. 2010, Springer. http://www.springerlink.com/openurl.asp?genre=article&id=doi:10.1007/s00170-009-2175-8.
137.
K. K. Kandaswamy, G. Pugalenthi,
E. Hartmann, K-U. Kalies, S. Möller,
P. N. Suganthan and T. Martinetz, “SPRED: A machine
learning approach for the identification of classical and non-classical
secretory proteins in mammalian genomes,” Biochemical and Biophysical
Research Communications, Vol. 391, No. 3, pp. 1306-1311, JAN 15 2010.
138.
M. F. Tasgetiren, P.
N. Suganthan, Q. K. Pan, "An Ensemble of Discrete Differential Evolution
Algorithms for Solving the Generalized Traveling Salesman Problem", Applied Mathematics and Computation, Vol. 215, No. 9, pp. 3356-3368, JAN 2010.
139.
K. Tang, G. Pugalenthi,
P. N. Suganthan, C. J Lanczycki, S. Chakrabarti,
"Prediction of functionally important sites from protein sequences using
sparse kernel least squares classifiers", Biochemical
and Biophysical Research Communications, Vol. 384, Issue 2, pp. 155-159,
DOI:10.1016/j.bbrc.2009.04.096, June 2009.
140.
A. Anand, P. N. Suganthan,
"Multiclass cancer classification by support vector machines with
class-wise optimized genes and probability estimates", J. of Theoretical Biology, 259(3):533-540, Aug. 2009, DOI: 10.1016/j.jtbi.2009.04.013. (Codes Available:
2009-Class-Wise-FS)
141.
R. Mallipeddi, P. N.
Suganthan, “Differential Evolution Algorithm with Ensemble of populations for
Global Numerical Optimization”, OPSEARCH, vol. 46, no. 2, pp. 184-213, June 2009, Springer.
142.
K. Krishna Kumar, G. Pugalenthi
, P. N. Suganthan, “DNA-Prot:
Identification of DNA binding proteins from protein sequence information using
random forest”, J of
Biomolecular Structure &
Dynamics, Volume
26, No. 6, pp. 679-686, June 2009.
143.
G. Pugalenthi,
K. Tang, P. N. Suganthan and S. Chakrabarti, "Identification of
structurally conserved residues of proteins in absence of structural homologs
using neural network ensemble", Bioinformatics, 2009 Jan
15;25(2):204-10. (Epub 2008 Nov 27).
144.
A. K. Qin, V. L. Huang,
and P. N. Suganthan, "Differential
evolution algorithm with strategy adaptation for global numerical optimization",
IEEE Trans. on Evolutionary Computations, DOI: 10.1109/TEVC.2008.927706, pp. 398-417, April,
2009 (SaDE - Self-adaptive differential Evolution). (Won "2012 IEEE CIS TEVC Outstanding Paper
Award") (Codes Available: 2009-IEEE-TEC-SaDE)
145.
M. K. Venu, R. Mallipeddi, P. N. Suganthan, Fiber Bragg grating sensor array interrogation using differential
evolution, Optoelectronics and Advanced
Materials - Rapid Communications, Vol. 2, No. 11, p.682-685, 2008.
146.
A. Anand, G. Pugalenthi, P. N. Suganthan,
"Predicting protein structural class by SVM with class-wise optimized
features and decision probabilities", J. of Theoretical Biology, 
DOI:10.1016/j.jtbi.2008.02.031, Vol. 253, Issue 2, July 2008, pp.
375-380 (Codes Available:
2008-JTB-Class_Wise-FS).
147.
G. Pugalenthi, K. Krishna Kumar, P. N. Suganthan and R. Gangal, "Identification of catalytic residues from
protein structure using support vector machine with sequence and structural
features", Biochemical and
Biophysical Research Communications, Vol 367/3 pp. 630-634, Feb 2008.
148.
G. Pugalenthi, P. N. Suganthan, R. Sowdhamini
and S. Chakrabarti, "MegaMotifBase:
a database of structural motifs in protein families and superfamilies",
Nucleic Acids Res. Vol. 36, pp. D218-221, Jan. 2008.
149.
G. Pugalenthi, E. K. Tang, P. N. Suganthan, G. Archunan, and R. Sowdhamini, "Machine learning approach for the
identification of odorant binding proteins from sequence-derived
properties", BMC Bioinformatics, Sep 19, Vol. 8, article 351, 2007 [This paper was selected
as a hot paper by Sciencewatch in 2009. http://sciencewatch.com/dr/nhp/2009/09marnhp/09marnhpRamET/
]
150.
J. J. Liang, C. C. Chan, V. L. Huang and P. N. Suganthan,
“Improving the performance of FBG sensor networks using dynamic multi-swarm
particle swarm optimizer”, J. of Optoelectronics and
Advanced Materials, Rapid-communications,
Issue 8, pp. 373-378, 2007.
151.
G. Pugalenthi, P. N. Suganthan, S. Sowdhamini,
S. Chakrabarti, “SMotif: A
server for structural motifs in proteins”, Bioinformatics 23:637-638 Mar. 2007.
152.
E. K. Tang, P. N. Suganthan and X. Yao,
"An Analysis of Diversity Measures", Machine Learning, supplementary materials
available here, 65 (1): 247-271 OCT. 2006.
153.
A. K. Qin, P. N. Suganthan
and M. Loog, "Generalized Null
Space Uncorrelated Fisher Discriminant Analysis for Linear Dimensionality
Reduction," Pattern Recognition, Vol. 39, pp. 1805-1808, Sept. 2006.
154.
J. J. Liang, P.
N. Suganthan, C. C. Chan and V. L. Huang, “Wavelength detection in FBG sensor
network using tree search dynamic multi-swarm particle swarm optimizer”, IEEE
Photonics Tech. Lets, 18(12):1305 - 1307, Jun 15, 2006 (Codes Available:
2006-IEEE-PTL-Tree-PSO).
155. S. Baskar, P. N. Suganthan, N. Q. Ngo, A. Alphones and R.
T. Zheng, “Design of Triangular FBG Filter for
Sensor Applications Using Covariance Matrix Adapted Evolution Algorithm,” 260(2):716-722, 15
Apr 2006, Optics Communications (Codes Available:
2006-Opt-Com-I & 2006-Opt-Com-II).
156.
J. J. Liang, A. K. Qin, P. N. Suganthan
and S. Baskar, "Comprehensive
Learning Particle Swarm Optimizer for Global Optimization of Multimodal
Functions", IEEE T. on Evolutionary Computation, Vol. 10, No.
3, pp. 281-295, June 2006 (Codes
Available: 2006-TEC-CLPSO).
157.
V. L. Huang, P. N. Suganthan and J. J.
Liang, “Comprehensive Learning Particle Swarm Optimizer for Solving Multiobjective
Optimization Problems”, Int. J of Intelligent Systems, Vol. 21, No. 2,
pp. 209-226, Feb. 2006, USA (Codes
Available: 2006-IJIS-MOCLPSO).
158.
J. J. Liang, S. Baskar, P. N. Suganthan
and A. K. Qin, “Performance Evaluation of Multiagent Genetic Algorithm”,
Volume 5, Number 1, pp. 83 – 96, Natural Computing, March 2006 (Codes Available:
2006-NACO-MAGA).
159.
E. K. Tang, P. N. Suganthan and X. Yao, “Gene
selection algorithms for microarray data using least squares support vector
machine”, BMC Bioinformatics, Feb. 2006,
7:95 (Codes Available:
2006-BMC-Bioinfo).
160.
S. Baskar, A.
Alphones, P. N. Suganthan and J. J. Liang, “Design of Yagi-Uda
Antennas Using Particle Swarm Optimization with new learning strategy”, IEE Proc. on Antenna and Propagation 152(5):340-346 OCT.
2005 (Codes Available:
2005-IEE-Yagi).
161.
Q-Y Zhu, A. K.
Qin, P. N. Suganthan, G-B Huang, "Evolutionary Extreme Learning
Machine", Pattern Recognition, pp 1759-1763, Vol. 38, No. 10,
October 2005.
162.
S. Baskar, A.
Alphones, P. N. Suganthan, N.
Q. Ngo and R. T. Zheng, "Design of Optimal Length Low Dispersion FBG
filter Using Covariance Matrix Adapted Evolution", IEEE Photonics
Technology Letters, 17(10):2119-2121 OCT 2005 (Codes Available:
2005-IEEE-PTL-opt-length-Oct).
163.
A. K. Qin
and P. N. Suganthan, “Enhanced neural gas network for prototype based
clustering”, Pattern Recognition, 38(8):1275-1288, August 2005 (Codes Available:
2005-PRJ-ENG)
164.
S. Baskar, A.
Alphones and P. N. Suganthan, “Genetic Algorithm Based Design of Reconfigurable
Antenna Array with Discrete Phase Shifters,” Microwave and Optical Technology Letters, 45(6):461-465, June
2005 (Codes Available:
2005-Antena-Array-MOTL).
165.
A. K. Qin
and P. N. Suganthan, “Initialization insensitive LVQ algorithm based on cost
function adaptation”, 38(5):773-776, Pattern Recognition, May 2005 (Codes Available:
2005-PRJ-H2MLVQ).
166.
E. K. Tang, P. N. Suganthan, X. Yao and
A. K. Qin, "Linear Dimensionality Reduction Using Relevance Weighted
LDA", 38(4):485-493, Pattern Recognition, April 2005 (Codes Available: 2005-PRJ-Ke-Tang).
167.
A. K. Qin, P. N. Suganthan and M. Loog, “Uncorrelated Heteroscedastic LDA Algorithm Based on
the Weighted Pairwise Chernoff Criterion”, 38(4):613-616, Pattern
Recognition, April 2005.
168.
S. Baskar, R. T.
Zheng, A. Alphones, N. Q. Ngo and P. N. Suganthan, “Particle Swarm
Optimization for the Design of Low-Dispersion Fiber
Bragg Gratings,” IEEE Photonics Technology Letters, 17(3):615-617, March
2005 (Codes Available:
2005-IEEE-PTL-Mar).
169.
A. K. Qin and P. N. Suganthan, “Robust growing neural
gas algorithm with application in cluster analysis”, Neural Networks special
issue on Recent Developments in Self-Organizing Systems, Vol. 17, No. 8-9,
pp. 1135-1148, Oct.-Nov. 2004 (Codes Available:
2004-NN-RNG).
170.
T. Lu and P.N. Suganthan, “An accumulation algorithm
for video shot boundary detection”, Multimedia Tools and Applications,
22(1):89-106, Jan. 2004, Kluwer, USA.
171.
A. S. Atukorale, T. Downs, P. N. Suganthan, “Boosting HONG
Networks", Neurocomputing, The Netherlands, Vol. 51, pp. 75-86,
April 2003.
172.
K. G. Khoo, P.
N. Suganthan, “Structural Pattern Recognition using Genetic Algorithms with
Specialised Operators", IEEE T. on Systems, Man and Cybernetics - B,
USA, 33(1):156-165, Feb. 2003.
173.
X. Cao, P. N.
Suganthan, “Video
Shot Motion Characterization based on Hierarchical Overlapped Growing Neural
Gas Networks", Multimedia Systems, 9(4):378-385, Oct. 2003.
174.
P. N. Suganthan, “Structural pattern recognition using
genetic algorithms”, Pattern Recognition, pp. 1883-1893, Vol. 35, No. 9,
Sept. 2002, UK.
175.
P. N. Suganthan,
“Shape
indexing using self-organising maps”, IEEE Trans. on Neural Networks,
pp. 835-840, Vol. 13 No. 4 July 2002, USA.
176.
K. G. Khoo, P.
N. Suganthan, “Evaluation of genetic operators and solution
representations for shape recognition by genetic algorithms”, Pattern
Recognition Letters, pp.1589-1597, 2002, The Netherlands.
177.
X. Cao, P. N.
Suganthan, “Neural Network based Temporal Video Segmentation", Int. J
of Neural Systems, World Scientific Press, Singapore, 2002.
178.
P. N. Suganthan, "Pattern classification using multiple hierarchical
overlapped self-organising maps", Pattern Recognition, November,
Vol. 34, pp. 2173-2179, 2001, UK.
179.
A. S. Atukorale and P. N. Suganthan, “Hierarchical
overlapped Neural-Gas network with application to pattern classification”,
Neurocomputing, November 2000.
180.
P. N. Suganthan,
“Structure
adaptive multilayer overlapped SOMs for labelled pattern classification”,
IEEE Trans on Neural Networks, January 1999.
181.
P. N. Suganthan,
E. K. Teoh and D. P. Mital, “Hopfield
Network with Constraint Parameter Adaptation for Overlapped Shape Recognition”,
IEEE Trans on Neural Networks, March 1999.
182.
P. N. Suganthan
and H. Yan, “Recognition of handprinted Chinese characters by constrained graph
matching”, Image and Vision Computing, Vol. 16, No. 3, 1998.
183.
P. N. Suganthan,
H. Yan E. K. Teoh and D. P. Mital, “Optimal encoding
of graph homomorphism energy using fuzzy information aggregation operators”, Pattern
Recognition, Vol. 31, No. 5, May 1998.
184.
P. N. Suganthan,
E. K. Teoh and D. P. Mital, “Optimal mapping of graph
homomorphism onto self-organising Hopfield network”, Image and Vision
Computing, 15(9):679-694, Sept. 1997.
185.
P. N. Suganthan,
E. K. Teoh and D. P. Mital, “Pattern recognition by
homomorphic graph matching using Hopfield network”, Image and Vision
Computing, 13(1):45-60, 1995.
186.
P. N. Suganthan,
E. K. Teoh and D. P. Mital, “A self-organising
Hopfield network for attributed relational graph matching”, Image and Vision
Computing, 13(1):61-73, Feb., 1995.
187.
P. N. Suganthan,
E. K. Teoh and D. P. Mital, “Pattern
recognition by graph matching using the Potts mean field theory network”, Pattern
Recognition, pp 997-1009, No. 7, Vol. 28, 1995.
Technical Reports (Send queries to epnsugan@ntu.edu.sg
)(Codes Available).
1.
P. N. Suganthan,
N. Hansen, J. J. Liang, K. Deb, Y.-P. Chen, A. Auger and S. Tiwari, "Problem
Definitions and Evaluation Criteria for the CEC 2005 Special Session on
Real-Parameter Optimization", Technical Report, Nanyang
Technological University, Singapore, May 2005 AND KanGAL
Report #2005005, IIT Kanpur, India.
2.
V. L. Huang, P.
N. Suganthan A. K. Qin and S. Baskar, "Multiobjective Differential
Evolution with External Archive and Harmonic
Distance-Based Diversity Measure", Technical Report, Nanyang Technological University, Singapore,
December, 2005.
3.
J. J. Liang, T. P. Runarsson, E. Mezura-Montes, M. Clerc, P. N.
Suganthan, C. A. Coello Coello, K. Deb, "Problem
Definitions and Evaluation Criteria for the CEC 2006 Special Session on
Constrained Real-Parameter Optimization”, Technical
Rep., Nanyang
Technological University, Singapore, 2006.
4.
V. L. Huang, A. K. Qin, K. Deb, E. Zitzler, P. N. Suganthan, J. J. Liang, M. Preuss and S. Huband, "Problem Definitions and Evaluation Criteria
for the CEC 2007 Special Session on Multi-objective evolutionary algorithms”, Technical Report, Nanyang Technological
University, Singapore, Feb 2007.
5.
K. Tang, X. Yao, P. N. Suganthan, C. MacNish, Y. P. Chen, C. M. Chen, and Z. Yang,
"Benchmark Functions for the CEC'2008 Special Session and Competition on
Large Scale Global Optimization," Technical Report, Nature Inspired
Computation and Applications Laboratory, USTC, China, & Nanyang
Technological University, Singapore, Nov. 2007.
6.
C. Li, S. Yang, T.
T. Nguyen, E. L. Yu, X. Yao, Y. Jin, H.-G. Beyer, and P. N. Suganthan,
"Benchmark Generator for CEC'2009 Competition on Dynamic
Optimization", Technical Report,
University of Leicester, University of Birmingham, Nanyang Technological
University, September 2008.
7.
Qingfu Zhang, Aimin
Zhou, S. Z. Zhao, P. N. Suganthan and Wudong Liu,
Santosh Tiwari, "Multiobjective Optimization Test Instances for the CEC
2009 Special Session and Competition", Technical
Report CES-887, University of Essex and Nanyang Technological University,
2008.
8.
K. Tang, Xiaodong Li, P. N. Suganthan, Z. Yang and T.
Weise, "Benchmark Functions for the CEC'2010 Special Session and
Competition on Large Scale Global Optimization," Technical Report,
Nature Inspired Computation and Applications Laboratory, USTC, China,
http://nical.ustc.edu.cn/cec10ss.php, & Nanyang Technological
University, 2009.
9.
R. Mallipeddi, P.
N. Suganthan, "Problem Definitions and Evaluation Criteria for the CEC
2010 Competition on Constrained Real-Parameter Optimization", Technical
Report, Nanyang Technological University, Singapore, 2010.
10.
S. Das, P. N. Suganthan, Problem
Definitions and Evaluation Criteria for CEC 2011 Competition on Testing
Evolutionary Algorithms on Real World Optimization Problems, Technical Report,
Jadavpur University, India & Nanyang Technological University,
Singapore, Dec 2010.
11. J.
J. Liang, B-Y. Qu, P. N. Suganthan, Alfredo G. Hernández-Díaz,
"Problem Definitions and
Evaluation Criteria for the CEC 2013 Special Session and Competition on
Real-Parameter Optimization", Technical Report
201212, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou
China and Technical Report, Nanyang Technological University, Singapore,
January 2013.
12. J..
J. Liang, B-Y. Qu, P. N. Suganthan, "Problem Definitions and
Evaluation Criteria for the CEC 2014 Special Session and Competition on Single
Objective Real-Parameter Numerical Optimization", Technical Report 201311,
Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China
and Technical Report, Nanyang Technological University, Singapore,
December 2013.
13. B. Liu, Q. Chen and Q. Zhang, J.
J. Liang, P. N. Suganthan, B. Y. Qu, "Problem
Definitions and Evaluation Criteria for Computationally Expensive Single
Objective Numerical Optimization", Technical Report, Computational Intelligence
Laboratory, Zhengzhou University, Zhengzhou, China and Technical Report,
Nanyang Technological University, Singapore, December 2013.
14. Q. Chen, B. Liu, Q. Zhang,
J. J. Liang, P. N. Suganthan, B. Y. Qu, "Problem Definition and Evaluation
Criteria for CEC 2015 Special Session and Competition on Bound Constrained
Single-Objective Computationally Expensive Numerical Optimization", Technical Report, Computational Intelligence
Laboratory, Zhengzhou University, Zhengzhou, China and Technical Report,
Nanyang Technological University, Singapore, Nov 2014.
15. J. J. Liang, B. Y. Qu, P. N.
Suganthan, Q. Chen, "Problem
Definitions and Evaluation Criteria for the CEC 2015 Competition on
Learning-based Real-Parameter Single Objective Optimization", Technical Report, Computational Intelligence
Laboratory, Zhengzhou University, Zhengzhou, China and Technical Report,
Nanyang Technological University, Singapore, Nov 2014.
- N. H. Awad, M. Z. Ali, J. J. Liang, B. Y. Qu and P. N.
Suganthan, "Problem
Definitions and Evaluation Criteria for the CEC 2017 Special Session and
Competition on Single Objective Bound Constrained Real-Parameter Numerical
Optimization," Technical Report, Nanyang Technological
University, Singapore, November 2016. (bound
constrained case)
- Guohua Wu, R. Mallipeddi, P. N. Suganthan, "Problem Definitions and Evaluation Criteria for the CEC 2017 Competition and Special Session on Constrained Single Objective Real-Parameter Optimization", Technical Report, Nanyang Technological University, Singapore, November 2016. (Single objective, constrained case)
Conference Publications (Please send queries to epnsugan@ntu.edu.sg
)
Updated on 4 Aug 2017
1. L.
Zhang, Jagannadan Varadarajan,
P.N. Suganthan, Pierre Moulin, Narendra Ahuja, "Robust Visual
tracking with oblique random forest". (CVPR 2017).[Project Page]
2. K
Rakesh, PN Suganthan, An Ensemble of
Kernel Ridge Regression for Multi-class Classification, Procedia Computer
Science 108, 375-383
3. X Qiu, PN Suganthan, GAJ Amaratunga, Short-term Electricity
Price Forecasting with Empirical Mode Decomposition based Ensemble Kernel
Machines, Procedia Computer Science 108, 1308-1317
4. PP
Biswas, NH Awad, PN Suganthan, MZ Ali, GAJ Amaratunga , Minimizing THD of
multilevel inverters with optimal values of DC voltages and switching angles
using LSHADE-EpSin algorithm, Evolutionary
Computation (CEC), 2017 IEEE Congress on, 77-82
5. NH
Awad, MZ Ali, PN Suganthan, RG Reynolds, AM Shatnawi,
A novel differential
crossover strategy based on covariance matrix learning with Euclidean neighborhood for solving real-world problems,
Evolutionary Computation (CEC), 2017 IEEE Congress on, 380-386 (Codes Available: 2017-CEC-DE-CML-ED)
6. PP
Biswas, PN Suganthan, GAJ Amaratunga, Optimal placement of
wind turbines in a windfarm using L-SHADE algorithm, Evolutionary
Computation (CEC), 2017 IEEE Congress on, 83-88
7. NH
Awad, MZ Ali, PN Suganthan, Ensemble sinusoidal
differential covariance matrix adaptation with Euclidean neighborhood
for solving CEC2017 benchmark problems, 2017 IEEE Congress on
Evolutionary Computation (CEC), 372-379
8. X Qiu, PN Suganthan, GAJ Amaratunga, Electricity load demand time series forecasting with Empirical Mode Decomposition based Random Vector Functional Link network, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).
9.
C Cubukcuoglu, A Kirimtat, MF
Tasgetiren, PN Suganthan, QK Pan, “Multi-objective harmony search algorithm for
layout design in theatre hall acoustics,” 2016 IEEE Congress on Evolutionary
Computation (CEC), 2280-2287.
10. NH Awad, MZ Ali, PN Suganthan, E Jaser,
“Differential evolution with stochastic fractal search algorithm for global
numerical optimization,” 2016 IEEE Congress on Evolutionary Computation (CEC),
3154-3161.
11. NH Awad, MZ Ali, PN Suganthan, RG Reynolds, “An ensemble
sinusoidal parameter adaptation incorporated with L-SHADE for solving CEC2014
benchmark problems,” 2016 IEEE Congress on Evolutionary Computation (CEC),
2958-2965. (Codes Available: 2016-CEC-Ens-Sin-LSHADE)
12. A Kirimtat, BK Koyunbaba,
I Chatzikonstantinou, S Sariyildiz,
et al, “Multi-objective optimization for shading devices in buildings by using
evolutionary algorithms,” 2016 IEEE Congress on Evolutionary Computation (CEC),
3917-3924.
13. M Paldrak, MF Tasgetiren, PN
Suganthan, QK Pan, “An ensemble of differential evolution algorithms with
variable neighborhood search for constrained function
optimization,” 2016 IEEE Congress on Evolutionary Computation (CEC), 2610-2617.
14. A. C. Palaninathan, Xueheng Qiu, P. N. Suganthan, “Heterogeneous Ensemble for
Power Load Demand Forecasting,” IEEE TENCON 2016, Nov 2016, Singapore.
15. P. P. Biswas, P. N. Suganthan, and Gehan A. J. Amaratunga.
“Optimization of Wind Turbine Rotor Diameters & Hub Heights in a Windfarm
using Differential Evolution Algorithm”, in 6th Int. Conf. on Soft Computing
for Problem Solving, Thapar University, India, Dec. 2016.
16. Y Ren, X Qiu, PN Suganthan, G
Amaratunga, “Detecting Wind Power Ramp with Random Vector Functional Link
(RVFL) Network”, 2015 IEEE Symposium Series on Computational Intelligence,
687-694, Dec, South Africa.
17. Nandar Lynn, Rammohan Mallipeddi, Ponnuthurai Nagaratnam
Suganthan, “Self-adaptive Ensemble Differential Evolution with Sampled
Parameter Values for Unit commitment”, SEMCCO 2015, India. .
18. L Zhang, PN Suganthan, “Visual Tracking with Convolutional
Neural Network,” 2015 IEEE International Conference on Systems, Man, and
Cybernetics (SMC).
19. N Lynn, PN Suganthan, “Modified Artificial Bee Colony
Algorithm with Comprehensive Learning reinitialization Strategy,” 2015 IEEE
International Conference on Systems, Man, and Cybernetics (SMC).
20. M. Ali, N. Awad, R. Duwairi, J. Albadarneh, R. Reynolds. P. Suganthan, “Cluster-Based
Differential Evolution with Heterogeneous Influence for Numerical
Optimization,” IEEE Congress on Evolutionary Computation, Japan, May
2015.
21. N Lynn, PN Suganthan, “Distance
Based Locally Informed Particle Swarm Optimizer with Dynamic Population Size,”
Proc. of the 18th Asia Pacific Symposium on Intelligent and Evolutionary
Systems, 2015.
22. L Zhang, Y Ren, PN Suganthan, “Towards
generating random forests via extremely randomized trees,” 2014
International Joint Conference on Neural Networks (IJCNN), 2645-2652.
23. X Qiu, L Zhang, Y Ren, P Suganthan,
G Amaratunga, “Ensemble
deep learning for regression and time series forecasting,” 2014 IEEE
Symposium Computational Intelligence in Ensemble Learning (CIEL). (Codes Available: 2014-CIEL2)
24. Y Ren, X Qiu, P Suganthan, “Empirical
mode decomposition based adaboost-backpropagation
neural network method for wind speed forecasting,” 2014 IEEE Symposium on
Computational Intelligence in Ensemble Learning (CIEL). (Codes Available: 2014-CIEL)
25. G Iacca, F Neri, F Caraffini, PN Suganthan, “A Differential Evolution
Framework with Ensemble of Parameters and Strategies and Pool of Local Search
Algorithms,” Applications of Evolutionary Computation, 615-626, 2014.
26. R Mallipeddi, G Wu, M Lee, PN Suganthan, “Gaussian
adaptation based parameter adaptation for differential evolution,” 2014
IEEE Congress on Evolutionary Computation (CEC), 1760-1767.
27. S. Biswas, S. Das, P. N. Suganthan and C. A. C Coello, “Evolutionary
Multiobjective Optimization in Dynamic Environments: A Set of Novel Benchmark
Functions,” IEEE Congress on Evolutionary Computation, Beijing, PRC,
July 2014. (Codes Available: 2014-CEC-Dyn-MOEA)
28. S. Hui and P. N. Suganthan, “Niching-based Self-adaptive
Ensemble DE with MMTS for solving Dynamic Optimization Problems,” IEEE
Congress on Evolutionary Computation, Beijing, PRC, July 2014.
29. M. F. Tasgetiren, S. Das, P. N. Suganthan and F. Neri, “A
Differential Evolution Algorithm for Real-Parameter Optimization Problems,” IEEE Congress on Evolutionary Computation, Beijing, July 2014.
30. M Ali, A. Morghem, J. AlBadarneh, R. Al-Gharaibeh, P.
N. Suganthan and R. Reynolds, “Cultural Algorithms Applied to the Evolution of
Robotic Soccer Team Tactics: A Novel Perspective, IEEE Congress on Evolutionary Computation, Beijing, PRC, July 2014.
31. R Mallipeddi, PN Suganthan, “Improved Adaptive Differential
Evolution Algorithm with External Archive,” Swarm, Evolutionary, and Memetic
Computing, 59-67, India, 2013.
32. D Basu, S Debchoudhury, KZ Gao, PN
Suganthan, “A Novel Improved Discrete ABC Algorithm for Manpower Scheduling
Problem in Remanufacturing,” Swarm, Evolutionary, and Memetic Computing, 59-67, India, 2013.
33. MF Tasgetiren, O Buyukdagli, QK
Pan, PN Suganthan, “A General Variable Neighborhood
Search Algorithm for the No-Idle Permutation Flowshop
Scheduling Problem,” Swarm, Evolutionary, and Memetic Computing, 59-67, India, 2013.
34. S Debchoudhury, D Basu, KZ Gao, PN
Suganthan, “Load Information Based Priority Dependent Heuristic for Manpower
Scheduling Problem in Remanufacturing,” Swarm, Evolutionary, and Memetic
Computing, 59-67, India, 2013.
35. K Gao, PN Suganthan, TJ Chua, TX Cai, CS Chong, “Hybrid
discrete harmony search algorithm for scheduling re-processing problem in
remanufacturing,” GECCO 2013, Netherlands.
36. R Kundu, R Mukherjee, S Debchoudhury,
S Das, PN Suganthan, “Improved CMA-ES with Memory based Directed Individual
Generation for Real Parameter Optimization,” IEEE Congress on Evolutionary Computation, Mexico, June 2013.
37. R Ye, PN Suganthan, N Srikanth, S Sarkar, “A hybrid
ARIMA-DENFIS method for wind speed forecasting,” ), 2013 IEEE International
Conference on Fuzzy Systems, 2013, India. (Codes Available: 2013-FUZZ)
38. R Mukherjee, S Debchoudhury, R Kundu, S Das, PN Suganthan, “Adaptive Differential Evolution
with Locality based Crossover for Dynamic Optimization,” IEEE Congress on Evolutionary Computation, Mexico, June 2013.
39. S. Sundar, P. N. Suganthan, “A Swarm intelligence approach to
flexible job shop scheduling problem with no-wait constraint in remanufacturing”,
Proc. Int. Conf. on AI and soft Computing, Poland, June 2013
40. M. F. Tasgetiren, Q.-K. Pan,
P. N Suganthan and I. E. Dizbay, “Metaheuristic
Algorithms for the Quadratic Assignment Problem,”Proc.
(CIPLS 2013) Computational Intelligence in Production and Logistics Systems,
Singapore, April 2013.
41. K Z Gao, P N Suganthan, T J
Chua, “An Enhanced Migrating Birds Optimization Algorithm for No-wait Flow Shop
Scheduling Problem,”, Proc. (CIShed 2013),
Computational Intelligence in Scheduling, April 2013, Singapore.
42. S. Biswas, S. Kundu, S. Das,
P. N. Suganthan and B. K. Panigrahi, “Migrating Forager Population in a
Multi-population Artificial Bee Colony Algorithm with Modified Perturbation
Schemes,” Proc. (SIS 2013), Swarm Intelligence Symposium April 2013, Singapore.
43. Le Zhang, Ye Ren and P. N.
Suganthan, “Instance Based Random Forest with Rotated Feature Space,” Proc.
(CIEL 2013), Symposium on Computational Intelligence and Ensemble Learning,
April 2013, Singapore.
44. S. Hui and P. N. Suganthan,
“Ensemble Crowding Differential Evolution with Neighborhood
Mutation for Multimodal Optimization,” Proc. (CIEL 2013), Symposium on
Computational Intelligence and Ensemble Learning, April 2013, Singapore. (Codes Available:
2013-CIEL-SSCI-nhood Crowding EPSDE)
45. J. Derrac,
S. Garcia, S. Hui, F. Herrera and P. N. Suganthan, “Statistical Analysis of
Convergence Performance Throughout the Evolutionary Search: A Case Study with SaDE-MMTS and Sa-EPSDE-MMTS,” Proc. (SDE 2013), Symposium
on Differential Evolution, April 2013, Singapore (Codes Available: 2013-SDE-SSCI-Sa.EPSDE)
46. N. Lynn and P. N. Suganthan,
“Comprehensive Learning Particle Swarm Optimizer with Guidance Vector
Selection,” Proc. (SIS 2013), Swarm Intelligence Symposium April 2013,
Singapore.
47. S. Biswas, S. Kundu, D. Bose,
S. Das and P. N. Suganthan, “A Modified Affinity-based Differential Evolution
with Restrictive Mutation and Synchronous Population Update,”, Proc. (SDE
2013), Symposium on Differential Evolution, April 2013, Singapore.
48. Y. Ren, L. Zhang and P. N.
Suganthan, “K-Nearest Neighbor based Bagging SVM
Pruning,” Proc. (CIEL 2013), Symposium on Computational Intelligence and
Ensemble Learning, April 2013, Singapore. (Codes Available: 2013-CIEL)
49. K. Z. Gao, P. N. Suganthan,
and T. J. Chua, “Pareto-based discrete harmony search algorithm for flexible job
shop scheduling problems,” Proc. Intelligent Systems Design and Applications
(ISDA), Dec 2012.
50.
Y. Ren and P. N. Suganthan, “A Kernel-Ensemble Bagging
Support Vector Machine,” Proc. Intelligent Systems Design and Applications
(ISDA), Dec 2012
51.
K Z Gao, P N Suganthan, T J Chua “Discrete Harmony
Search Algorithm for Dynamic FJSSP in Remanufacturing Engineering”, Proc.
Swarm, Evolutionary, and Memetic Computing, pp. 9-16, Dec 2012, BBSR, India.
52.
P N Suganthan, Differential
evolution algorithm: recent advances,” Proc. Theory and Practice of Natural
Computing, pp. 30-46, Tarragona, Spain, Oct 2012.
53.
Y. Ren, P N Suganthan, “Comparison of Bagging-based
Ensemble Classifiers,”, Fusion 2012, Singapore, July 2012.
54.
B
Y Qu, J Liang, P Suganthan, T Chen, “Ensemble of clearing differential
evolution for multi-modal optimization,” Advances in Swarm Intelligence, pp.
350-357,
Proc. ICSI, June 2012, PR-China.
55.
J
Li, Q K Pan, P Suganthan, M Tasgetiren, “Solving Fuzzy Job-Shop Scheduling
Problem by a Hybrid PSO Algorithm,” Proc. Swarm and Evolutionary Computation,
pp. 275-282, a part of ICAISC 2012, April-May 2012, Poland.
56.
M
Tasgetiren, QK Pan, P Suganthan, O Buyukdagli, “A
variable iterated greedy algorithm with differential evolution for solving
no-idle flowshops,” Swarm and Evolutionary
Computation, pp. 128-135, a part of ICAISC 2012, April-May 2012, Poland.
57.
S. Hui, P. N. Suganthan, “Ensemble Differential
Evolution with Dynamic Subpopulations and Adaptive Clearing for solving Dynamic
Optimization Problems”, IEEE
Congress on Evolutionary Computation,
Brisbane, Australia, June 2012.
58.
J. J. Liang, B-Y. Qu and P. N. Suganthan, “Dynamic Multi-Swarm Particle
Swarm Optimization for Multi-Objective Optimization Problems”, IEEE Congress on Evolutionary Computation,
Brisbane, Australia, June 2012.
59.
S-Z. Zhao, P. N. Suganthan, “Comprehensive Comparison of Convergence
Performance of Optimization Algorithms based on Nonparametric Statistical
Tests”, IEEE Congress on Evolutionary Computation,
Brisbane, Australia, June 2012.
60.
A. Rajasekhar, S. Das and P. N. Suganthan, “Design of Fractional Order
Controller for a Servohydraulic Positioning System
with Micro Artificial Bee Colony Algorithm”, IEEE Congress on Evolutionary Computation,
Brisbane, Australia, June 2012.
61.
Md Nasir, S. Sengupta, S. Das, P. N. Suganthan, “An improved
Multi-objective Optimization Algorithm based on Fuzzy Dominance for Risk
Minimization in Biometric Sensor Network”, IEEE Congress on
Evolutionary Computation, Brisbane,
Australia, June 2012.
62.
S. Roy, Sk Minhazul
Islam, S. Ghosh, S-Z. Zhao, P.N. Suganthan and Swagatam Das, “Design of Two
Channel Quadrature Mirror Filter Bank: A Multi-objective approach”, Swarm, Evolutionary and Memetic
Computing Conf (SEMCCO 2011), pp. 239-247, LNCS
Vol. 7077, DOI: 10.1007/978-3-642-27242-4_28, India.
63.
Sk Minhazul Islam,
S. Ghosh, S. Roy, S-Z Zhao, P. N. Suganthan and Swagatam Das, “Synthesis and
Design of Thinned Planar Concentric Circular Antenna Array- A Multi-Objective
Approach”, Swarm,
Evolutionary and Memetic Computing Conf (SEMCCO 2011), pp.
182-190, LNCS Vol. 7077, DOI: 10.1007/978-3-642-27242-4_22, India.
64.
Saurav
Ghosh, Subhrajit Roy, Sk Minhazul Islam, Shizheng Zhao, P.N. Suganthan and Swagatam Das,
“Non-uniform Circular-shaped Antenna Array Design and Synthesis- A
Multi-Objective Approach”, Swarm,
Evolutionary and Memetic Computing Conf (SEMCCO 2011), pp.
223-230, LNCS Vol. 7077, DOI: 10.1007/978-3-642-27242-4_26, India.
65.
M. Ankush, A.
K. Das, P. Mukherjee, S. Das and P. N. Suganthan, “Modified Differential
Evolution with Local Search Algorithm for Real World Optimization”, IEEE
Congress on Evolutionary Computation, pp. 1565-1572, New Orleans, USA, June
2011.
66.
R. Mallipeddi, G. Iacca, P. N. Suganthan, F. Neri and E. Mininno,
“Ensemble Strategies in Compact Differential Evolution”, IEEE Congress on
Evolutionary Computation, pp. 1972 - 1977, New Orleans, USA, June 2011.
67.
R. Mallipeddi and
P. N. Suganthan, “Ensemble Differential Evolution Algorithm for CEC2011
Problems”, IEEE Congress on Evolutionary Computation, pp. 1557 - 1564,
New Orleans, USA, June 2011.
68.
B. Y. Qu, V. R. Pandi, B. K. Panigrahi, P. N. Suganthan and S. Z. Zhao,
“Two Local Best Based Multi Objective Particle Swarm Optimization to Solve
Environmental Economic Dispatch Problem”, Proc. ICETECT 2011 India, March 2011.
69.
B. Y. Qu and P. N.
Suganthan, “Multi-objective differential evolution based on the summation of
normalized objectives and improved selection method,” SDE-2011 IEEE Symposium
on Differential Evolution, pp. 1-8, Paris, France, DOI:
10.1109/SDE.2011.5952065,
April 2011. (Codes Available: 2011-SDE-Fast-Sroting-MOEA)
70.
M. F. Tasgetiren,
O. Bulut, Q-K Pan, P. N. Suganthan, “A Differential
Evolution Algorithm for the Median Cycle Problem”, SDE-2011 IEEE Symposium on
Differential Evolution, pp. 1-8, Paris, France, April 2011.
71.
S. Sardar, S. Maity, S. Das, P. N. Suganthan, “Constrained Real Parameter
Optimization with a Gradient Repair based Differential Evolution Algorithm”,
SDE-2011 IEEE Symposium on Differential Evolution, pp. 1-8, Paris, France,
April 2011.
72.
S. Ghosh, S. Roy,
M. Islam, S. Das, P. N. Suganthan, “A Differential Covariance matrix Adaptation
Evolutionary Algorithm for Global Optimization,” SDE-2011, IEEE Symposium on
Differential Evolution, Paris, France, pp. 1-8, April 2011.
73.
G. Iacca, R. Mallipeddi, E. Mininno,
F. Neri and P. N. Suganthan, "Super-fit and Population Size Reduction
Mechanisms in Compact Differential Evolution", Proc. of the IEEE
Symposium on Memetic Computing, pp. 1-8, Paris, April 2011.
74.
G. Iacca, R. Mallipeddi, E. Mininno,
F. Neri and P. N. Suganthan, "Global Supervision for Compact Differential
Evolution", Proc. of the IEEE Symposium on Differential Evolution,
Paris, pp. 1-8, April 2011.
75.
B. Y. Qu, P. N.
Suganthan and S. Z. Zhao, “Current Based Fitness Euclidean-distance Ratio
Particle Swarm Optimizer for Multi-modal Optimization”, Second World
Congress on Nature and Biologically Inspired Computing (NaBIC2010),
Kitakyushu, Japan, Dec 15 – 17, 2010
76.
S. Z. Zhao, P. N. Suganthan
and B. Y. Qu, “Multiobjective Particle Swarm Optimizer with Dynamic
epsilon-dominance Sorting”, Second World Congress on Nature and Biologically
Inspired Computing (NaBIC2010), Kitakyushu, Japan, Dec 15 – 17, 2010.
77.
R. Mallipeddi and P. N.
Suganthan, “Differential
Evolution Algorithm with Ensemble of Parameters and Mutation and Crossover
Strategies", Swarm Evolutionary and Memetic Computing Conference, LNCS, Vol. 6466, pp. 71-78,
Chennai, India 2010. (Codes
Available: 2010-SEMCCO-EPSDE)
78.
R. Mallipeddi, Ashu Verma, P. N. Suganthan, Bijaya K. Panigrahi, P. R. Bijwe, Constraint Handling in Transmission Network
Expansion Planning, Proc. Swarm, Evolutionary and Memetic Computing Conf,
Chennai, India, p. 709-717, Dec. 2010
79.
B. Y. Qu, P. Gouthanan, and P. N. Suganthan, “Dynamic Grouping Crowding
Differential Evolution with Ensemble of Parameters for Multi-modal
Optimization”, SEMCCO:
Swarm, Evolutionary and Memetic Computing Conf, Chennai, India,
Dec 2010.
80.
S. Z. Zhao, P. N.
Suganthan and S. Das, “Self-adaptive Differential Evolution with Modified
Multi-trajectory Search for CEC'2010 Large Scale Optimization” International
Conference on Swarm, Evolutionary and Memetic Computing (SEMCCO 2010),
Chennai, India, Dec. 16-18, 2010.
81.
B. Y. Qu, V. R. Pandi, B. K. Panigrahi and P. N. Suganthan, “Multi
Objective evolutionary programming to Solve Environmental Economic Dispatch
Problem”, Proc. ICARCV, Dec 2010.
82.
S. Z. Zhao, B. Y.
Qu, M. Willjuice Iruthayarajan, S. Baskar and P. N. Suganthan, “Multi-objective
Robust PID Controller Tuning using Multi-objective Differential Evolution” the
Eleventh International Conference on Control, Automation, Robotics and Vision
(ICARCV 2010), Singapore, December, 2010.
83.
S. Z. Zhao, P. N.
Suganthan, S. Pal, S. Das and A. Basak,
“Multi-Objective Design of Monopulse Antenna with
Two-lbests based Multi-objective Particle Swarm
Optimizer”, 1st Int. Conf. On Computational Problem-Solving (ICCP2010),
Li Jiang, China, Dec 3-5, 2010. (One of the Best 5 Student Paper Awards) (Codes Available:
2010-ICCP-Monopulse)
84.
B. Y. Qu and P. N.
Suganthan, “Modified species-based differential evolution with self –adaptive
radius for multi-modal optimization,” 1st Int. Conf. On Computational
Problem-Solving, Li Jiang, China, Dec 3-5, 2010.
85.
S. Z.
Zhao, P. N. Suganthan and S. Das, “Dynamic Multi-Swarm Particle Swarm Optimizer
with Sub-regional Harmony Search”, IEEE Congress on Evolutionary
Computation, Barcelona, Spain, pp. 1983-1990,
July 2010. (Codes
Available: 2010-CEC-SHS or 2010-CEC-DMS-PSO)
86. R. Mallipeddi and P. N. Suganthan, “Differential Evolution with Ensemble
of Constraint Handling Techniques for Solving CEC 2010 Benchmark Problems”, IEEE Congress
on Evolutionary Computation, Barcelona,
Spain, pp. 1907-1914, July 2010. Upgraded version with feasibility rates available.
87. B. Y. Qu and P. N. Suganthan, “Novel
Multimodal Problems and Differential Evolution with Ensemble of Restricted
Tournament Selection”, IEEE Congress on Evolutionary
Computation, Barcelona, Spain, pp. 3480-3486,
July 2010. (Codes Available: 2010-CEC-niching-problems)
88. M. F. Tasgetiren, P. N. Suganthan, Q-K Pan, R. Mallipeddi and S. Sarman, “An Ensemble of Differential Evolution Algorithms
for Constrained Function Optimization”, IEEE Congress on Evolutionary
Computation, Barcelona, Spain, pp. 967-975,
July 2010.
89. B. Y. Qu and P. N. Suganthan, “Constrained Multi-Objective Optimization
Algorithm with Diversity Enhanced Differential Evolution”, IEEE Congress
on Evolutionary Computation, Barcelona,
Spain, pp. 1675-1679, July 2010.
90. G. G. Roy, P. Chakroborty, S. Z. Zhao, S. Das
and P. N. Suganthan, “Artificial Foraging Weeds for Global Numerical Optimization
over Continuous Spaces”, IEEE Congress on Evolutionary
Computation, Barcelona, Spain, pp. 1189-1196,
July 2010.
91. A. Anand, N. R. Pal and P. N. Suganthan, “Integration of Functional
Information of Genes in Fuzzy Clustering of Short Time Series Gene Expression
Data”, IEEE Congress on Evolutionary Computation, Barcelona, Spain, pp. 3002-3009, July 2010.
92. G. Kandavanam, D. Botvich,
S. Balasubramaniam and P. N. Suganthan, “Achieving
High Robustness and Performance in Performing QoS-aware Route Planning for IPTV
Networks”, IEEE Congress on Evolutionary Computation, Barcelona, Spain, pp. 749-756, July 2010.
93. M. F.
Tasgetiren, Q-K Pan, P. N. Suganthan and A. H-L Chen, “A Discrete Artificial
Bee Colony Algorithm for the Permutation Flow Shop Scheduling Problem with Total
Flow time Criterion”, IEEE Congress on Evolutionary Computation, Barcelona, Spain, pp. 137-144, July 2010.
94.
Q-K Pan,
M F. Tasgetiren, P N Suganthan and Y-C Liang, “Solving Lot-streaming Flow Shop
Scheduling Problems Using a Discrete Harmony Search Algorithm”, IEEE Congress
on Evolutionary Computation, Barcelona,
Spain, pp. 4134-4139, July 2010.
95.
Q. K.
Pan, P. N. Suganthan, M. F. Tasgetiren, “A
Harmony Search Algorithm with Ensemble of Parameter Sets”, IEEE
Congress on Evolutionary Computation, pp. 1815-1820, Norway, May 2009.
96.
E. L. Yu,
P. N. Suganthan, “Evolutionary
Programming with Ensemble of External Memories for Dynamic Optimization”, IEEE Congress on Evolutionary Computation, Norway, pp. 431-438, May 2009. (Codes
Available: 2009-CEC-DynEP)
97.
S. Z.
Zhao, P. N. Suganthan, “Diversity Enhanced Particle Swarm Optimizer for Global
Optimization of Multimodal Problems”, IEEE
Congress on Evolutionary Computation, Norway,
pp. 590-597, May 2009.
98.
V. L.
Huang, S. Z. Zhao, R. Mallipeddi, P. N. Suganthan, “Multi-objective
Optimization Using Self-adaptive Differential Evolution Algorithm”, IEEE Congress on Evolutionary Computation, pp. 190-194, Norway, May 2009. (Codes
Available: 2009-CEC-MOSaDE)
99.
R.
Mallipeddi, P. N. Suganthan, “Diversity Enhanced Adaptive Evolutionary
Programming for Solving Single Objective Constrained Optimization Problems”, IEEE Congress on Evolutionary Computation, pp.
100.
B. Y. Qu,
P. N. Suganthan, “Multi-objective Evolutionary Programming without
Non-domination Sorting is up to Twenty Times Faster”, IEEE Congress on Evolutionary Computation, pp.
101.
M. F. Tasgetiren,
Quan-Ke Pan, P. N Suganthan and Yun-Chia Liang, “A
Differential Evolution Algorithm with Variable Parameter Search for
Real-Parameter Continuous Function Optimization ”, IEEE Congress on Evolutionary Computation, Norway, pp. 1247-1254, May 2009.
102.
M. F. Tasgetiren P. N
Suganthan, T. J. Chua and A. Al-Hajri, “Differential
Evolution Algorithms for the Generalized Assignment Problem”, IEEE Congress on Evolutionary Computation, Norway, pp.
103.
R. Mallipeddi, P. N.
Suganthan, “Evaluation of Novel Adaptive Evolutionary Programming
on Four Constraint Handling Techniques ”, IEEE
Congress on Evolutionary Computation, pp. 4045-4052, Hong Kong,
June 2008.
104.
R. Mallipeddi, P. N.
Suganthan, “Empirical Study on the Effect of Population Size on Differential
Evolution”, IEEE Congress on Evolutionary
Computation, pp.
105.
E. L. Yu, P. N. Suganthan,
“Empirical Comparison of Niching Methods on Hybrid Composition Functions”, IEEE
Congress on Evolutionary Computation, pp. 2194-2201, Hong
Kong, June 2008.
106.
Q. K. Pan, M. F. Tasgetiren,
Y. C. Liang, P. N Suganthan, “Upper Bounds on Taillard's
Benchmark Suite for the No-wait Flowshop Scheduling
Problem with Makespan Criterion”, IEEE Congress on Evolutionary
Computation, pp.
107.
T. A. A. Victoire, P. N. Suganthan, “Differential Evolution and Evolutionary Programming
for Solving Non-convex Economic Dispatch Problems”, IEEE Congress on Evolutionary Computation, pp. 1785-1791, Hong Kong, June 2008.
108.
S. Z. Zhao, J. J. Liang, P.
N. Suganthan, M. F. Tasgetiren, “Dynamic Multi-swarm Particle Swarm Optimizer
with Local Search for Large Scale Global Optimization”, IEEE
Congress on Evolutionary Computation, pp.
109.
A. Anand, G. B.
Fogel, G. Pugalenthi, P. N. Suganthan, “Prediction of
Transcription Factor Families Using DNA Sequence Features”, P. 3rd IAPR Int. Conf. on Pattern Recognition
in Bioinformatics, Oct. 15-17, 2008 Melbourne, AUSTRALIA, Lecture Notes in
BIOINFORMATICS Vol. 5265 pp. 154-164, 2008.
110.
Kandavanam G, Botvich D, Balasubramaniam S, P. N. Suganthan and M. F. Tasgetiren, “A
Dynamic Bandwidth Guaranteed Routing Using Heuristic Search for Clustered
Topology”, 2nd Int. Symp. on Advanced Networks and
Telecommunication Systems, Dec. 15-17, Mumbai, INDIA, pp.
109-111, 2008.
111.
C. Duerr,
T. Fuehner and P. N. Suganthan, “LisBON:
A framework for parallelisation and hybridisation of optimisation algorithms”, IEEE
Congress on Evolutionary Computation, pp. 1717-1724, Sept. 2007, Singapore.
112.
M. F. Tasgetiren, P. N Suganthan and Q.-K. Pan, “A Genetic Algorithm for
the Generalized Traveling Salesman Problem”, IEEE Congress on Evolutionary Computation, pp. 2382-2389, Sept. 2007, Singapore.
113.
V. L.
Huang, A, K. Qin, P. N. Suganthan and M. F. Tasgetiren, “Multi-objective
Optimization based on Self-adaptive Differential Evolution Algorithm”, IEEE Congress on Evolutionary Computation, pp. 3601-3608, Sept. 2007, Singapore. (Codes
Available: 2007-CEC-MOSaDE)
114.
T. A. A.
Victoire and P. N Suganthan, “Improved MOCLPSO Algorithm for
Environmental/Economic Dispatch”, IEEE
Congress on Evolutionary Computation, pp.
3072-3076, Sept. 2007, Singapore.
115.
A. Anand,
P. N. Suganthan and K. Deb, “A novel fuzzy and multiobjective evolutionary algorithm
based gene assignment for clustering short time series expression data”, IEEE Congress on Evolutionary Computation, pp. 297-304, Sept.
2007, Singapore.
116.
G. Kandavanam, D. Botvich, S. Balasubramaniam, P.
N. Suganthan and W. Donnelly, “A Multi-layered Solution for supporting ISP
traffic demand using Genetic Algorithm”, IEEE Congress on Evolutionary Computation, pp. 2032-2039, Sept.
2007, Singapore.
117.
M. F. Tasgetiren, P. N. Suganthan, Q. K. Pan, “A Discrete Particle Swarm Optimization Algorithm for the
Generalized Traveling Salesman Problem”, Genetic and Evolutionary Computation
Conference, pp. 158-165, London, UK, July 07-11, 2007.
118.
M. F. Tasgetiren, Q. K.
Pan, Y. C. Liang, P. N. Suganthan, “A Discrete Differential Evolution Algorithm
for the No-Wait Flowshop Scheduling Problem with
Total Flow time Criterion”, IEEE Symposium on Computational Intelligence in
Scheduling, pp. 251-258, Hawaii, April 2007.
119.
M. F. Tasgetiren, Q-K Pan, Y-C. Liang, P. N Suganthan,
“A Discrete Differential Evolution Algorithm for the Total Earliness and
Tardiness Penalties with a Common Due Date on a Single-Machine”, IEEE Symposium on Computational
Intelligence in Scheduling, pp. 271-278, Hawaii, April 2007.
120.
J. J. Liang and P. N. Suganthan, “Dynamic Multi-Swarm
Particle Swarm Optimizer with a Novel Constraint-Handling Mechanism”, IEEE
Congress on Evolutionary Computation, pp. 9-16, July 2006, Canada. (Codes
Available: 2006-CEC-DMS-PSO)
121.
M. F. Tasgetiren and P. N. Suganthan, “A
Multi-Populated Differential Evolution Algorithm for Solving Constrained
Optimization Problems”, IEEE Congress on
Evolutionary Computation, pp. 33-40, July 2006, Canada.
122.
V. L. Huang, A. K. Qin and P. N. Suganthan,
“Self-adaptive Differential Evolution Algorithm for Constrained Real-Parameter
Optimization”, IEEE Congress on
Evolutionary Computation, pp. 7-24, July 2006, Canada. (Codes
Available: 2006-CEC-Constr-SaDE)
123.
A. K. Qin, P. N.
Suganthan and M. Loog, “Efficient Feature Extraction
Based on Regularized Uncorrelated Chernoff Discriminant Analysis”, 18th
International Conference on Pattern Recognition (ICPR 2006), pp. 125-128, Hong Kong, Aug.
2006,
124.
A. Anand, G. Fogel, E. K. Tang and P. N. Suganthan,
“Feature selection approach for quantitative prediction of transcriptional
activities”, IEEE Symposium on
Computational Intelligence in Bioinformatics and Computational Biology, pp. 57-62, Sept. 2006, Toronto,
Canada.
125.
J. J. Liang and P. N. Suganthan, “Adaptive
Comprehensive Learning Particle Swarm Optimizer with History Learning”, 6th
International Conference on Simulated Evolution and Learning, LNCS, Vol. 4247, pp. 213-220, Oct. 2006, China
126.
Y. Li, P.
N. Suganthan, et al., “Genetic Algorithms for Silhouette Matching”, 9th
International Conference on Control, Automation, Robotics and Vision, pp. 1712-1717,
Singapore, Dec. 2006.
127.
A. K. Qin, P. N.
Suganthan, C. H. Tay and H. S. Pa, “Personal Identification System based on
Multiple Palmprint Features”, 9th
International Conference on Control, Automation, Robotics and Vision, pp. 1717-1723,
Singapore, Dec. 2006.
128.
A. K. Qin, S. Y. M.
Shi, P. N. Suganthan and M. Loog, “Enhanced Direct
Linear Discriminant Analysis for Feature Extraction on High Dimensional Data”, Proc.
American Association for AI, July 2005.
129.
J. J. Liang, and P. N.
Suganthan, "Dynamic
Multi-Swarm Particle Swarm Optimizer," IEEE Swarm
Intelligence Symposium, pp. 124-129, Pasadena,
CA, USA, June 2005. (Codes
Available: 2005-IEEE-SIS-DMS-PSO
& 2008-DMS-PSO-fun)
130.
J. J. Liang, P. N.
Suganthan and K. Deb, "Novel
composition test functions for numerical global optimization,"
IEEE Swarm Intelligence Symposium, pp. 68-75, Pasadena,
CA, USA, June 2005.
131.
J. J. Liang and P. N.
Suganthan, “Dynamic Multi-Swarm Particle Swarm Optimizer with Local
Search," IEEE Congress on Evolutionary Computation, pp. 522-528, Edinburgh,
UK, Sept. 2005.
132.
A. K. Qin and P. N. Suganthan, “Self-adaptive
Differential Evolution Algorithm for Numerical Optimization”, IEEE Congress on Evolutionary
Computation,
pp. 1785-1791, Edinburgh, UK, Sept. 2005.
133.
J. J. Liang, C. C.
Chan, V. L. Huang, & P. N. Suganthan, "Improving the performance of a
FBG sensor network using a novel dynamic multi-swarm particle swarm optimizer
algorithm." SPIE Symposium on Optics East, Boston,
Massachusetts USA, Oct. 2005.
134.
E. K. Tang, P. N.
Suganthan and X. Yao, “Feature Selection for Microarray Data Using Least
Squares SVM and Particle Swarm Optimization”, IEEE Symposium on
Computational Intelligence for Bioinformatics and Computational biology,
pp. 9-16, La Jolla, CA, USA, Nov. 2005.
135.
S. Baskar, A. Alphones,
P. N. Suganthan, “Design
of reconfigurable antenna Array using improved multiagent GAs”, Asia
Pacific Microwave Conf. 2004, Dec. New Delhi, India.
136.
A.
K. Qin and P. N. Suganthan, “A robust neural gas algorithm for clustering
analysis”, International
Conference on Intelligent Sensing and Information Processing (ICISIP2004), pp. 342-347, Chennai, India, Jan. 2004.
137.
S. Y. M. Shi, P. N.
Suganthan and K. Deb, “Multi class protein fold recognition using
multi-objective evolutionary algorithms”, IEEE Symposium on Computational
Intelligence in Bioinformatics and Computational Biology, San Diego, USA,
Oct. 2004.
138.
A.
K. Qin and P. N. Suganthan, “Growing generalized learning vector quantization
with local neighborhood adaptation rule”, 2nd
IEEE International Conference on Intelligent Systems,
pp. 524-529, Sofia, Bulgaria, June 2004.
139.
A.
K. Qin and P. N. Suganthan, “Kernel Neural Gas Algorithms with Application to
Cluster Analysis”, 17th International Conference on
Pattern Recognition (ICPR’04), pp. 617-620, Cambridge, UK Aug. 2004.
140.
A.
K. Qin and P. N. Suganthan, “A Novel Kernel Prototype-Based Learning
Algorithm”, 17th International Conference on Pattern Recognition (ICPR’04), pp. 621-624, Cambridge, UK Aug. 2004.
141.
S.
Baskar, A. Alphones, P. N. Suganthan, “Concurrent PSO and FDR-PSO based
reconfigurable Phase-Differentiated Antenna Array Design”, IEEE
Congress on Evolutionary Computation, pp. 2173-2179, Oregon, Portland, USA, June 2004.
142.
S. Baskar, and P. N. Suganthan, “A Novel Concurrent Particle Swarm
Optimization”, IEEE Congress on
Evolutionary Computation, pp. 792-796, Oregon, Portland, USA, June 2004.
143.
E.
K. Tang, P. N. Suganthan and X. Yao, “Generalized LDA Using Relevance Weighting
and Evolution Strategy”, IEEE Congress on
Evolutionary Computation, pp. 2230-2234, Oregon, Portland, USA, June 2004.
144.
E. Lim, K A Toh, P N Suganthan, X. D. Jiang, W. Y. Yau, “Fingerprint image quality analysis”, International
Conference on Image Processing (ICIP 2004), pp. 1241-1244, Singapore, Oct. 2004.
145.
A.
K. Qin, P. N. Suganthan and J. J. Liang “A new generalized LVQ algorithm via
harmonic to minimum distance measure transition”, IEEE
International Conference on Systems, Man and Cybernetics (SMC 2004), pp. 4821-4825, The Hague, The Netherlands, Oct. 2004.
146.
J. J. Liang, A. K. Qin,
P. N. Suganthan and S. Baskar, “Particle Swarm Optimization Algorithms with
Novel Learning Strategies” IEEE International
Conference on Systems, Man and Cybernetics
(SMC2004), pp. 3659-3664, The Hague, The Netherlands,
Oct. 2004.
147.
J. J. Liang, A. K. Qin,
P. N. Suganthan and S. Baskar, “Multi-exemplars
Particle Swarm Optimization Algorithm”, 11th International Conference on Neural Information
Processing (ICONIP2004), Vol. 3316, pp.
230-235, Calcutta, India, Nov., 2004.
148.
E. K. Tang, P. N.
Suganthan and X. Yao, "Nonlinear Feature Extraction Using Evolutionary
Algorithm", 11th International Conference
on Neural Information Processing (ICONIP2004), Vol. 3316, pp. 1014-1019, Calcutta,
India, Nov. 2004.
149.
S.
Y. M. Shi, P. N. Suganthan, ”Feature Analysis and Classification of Protein
Secondary Structure Data”, Springer’s
Lecture Notes in Computer Science, Vol. 2714 pp. 1151-1158 (ICANN/ICONIP 2003), Istanbul, Turkey, June 2003.
150.
S.
Y. M. Shi, P. N. Suganthan, ”Unsupervised Similarity-based Feature Selection
using Heuristic Hopfield Neural Networks”, International
Joint Conference on Neural Networks (IJCNN’03), Portland, Oregon, USA, vol. 3, pp 1838-1843, July
2003.
151.
A.
S. Atukorale,T. Downs and P. N. Suganthan,
"Improving the performance of the HONG network with boosting", Int.
Joint Conf. on Neural Networks (IJCNN’02), Vol, 2, pp. 1753 -1756, May
2002.
152.
X.
Cao and P. N. Suganthan, "Hierarchical overlapped growing neural gas
networks with applications to video shot detection and motion
characterization", International Joint Conference
on Neural Networks
(IJCNN'02),
Vol, 2, pp. 1069 -1074,
May 2002.
153.
K.
G. Khoo and P. N. Suganthan, "Objective function decomposition within
genetic algorithm", IEEE Congress on Evolutionary Computation, (CEC '02), Vol. 1, pp. 356 -359, May 2002
154.
K.
G. Khoo and P. N. Suganthan, “Clearing Procedure as a Niching Approach to
Multiple Relational Graphs Matching, In Proc. SEAL’02, Singapore,
November 2002.
155.
A.
S. Atukorale, T. Downs, P. N. Suganthan, ”Improving the Performance of the HONG Network with Boosting”,
International Joint Conference on Neural Networks (IJCNN’02), Vol. 2, pp. 1753-1756, May 2002.
156.
T. Lu and P. N. Suganthan, “An adaptive
cumulation algorithm for video shot detection”, P. Int. Symposium on Intelligent Multimedia, Video & Speech Processing, in May 2001 in Hong Kong.
157.
K.
G. Khoo and P. N. Suganthan, “Multiple Relational Graphs mapping using Genetic
Algorithms”, In the Proc. Of the Congress on Evolutionary
Computations, May 2001, Korea (INVITED).
158.
P.
N. Suganthan, “SHAPESOM”, Proc. Of the Workshop on SOM, June
2001, UK.
159.
X.
Cao and P. N. Suganthan, “Video sequence boundary detection using neural GAS
networks”, ICANN’01, August 2001, Austria.
160.
H.
Yin, P. N. Suganthan and S. M. Krishnan, “Evaluation of distance measures for Image
Retrieval Using Self-Organising Map”, ICANN’01, August 2001, Austria.
161.
K.
G. Khoo and P. N. Suganthan, “Multiple Relational Graphs Mapping Using Genetic
Algorithms With Genetic Engineering Style Operators”, ICICS’01, October 2001, Singapore.
162.
P.
N. Suganthan and X. Cao, “Digital video sequence segmentation using
self-organising maps”, ICONIP’01, November 2001, China.
163.
P.
N. Suganthan, “Shape indexing using relational vectors and neural networks”, ICONIP’01,
November 2001, China.
164.
A.
S. Atukorale, P. N. Suganthan and T Downs, "On
the Performance of the HONG Network for Pattern Classification", P.
Int. Joint Conf. on Neural Networks, Como, Italy, paper 590, July 2000.
165.
P.
N. Suganthan, “Relational Graph Matching using Self-Organising Maps”, P.
ICONIP’2000, Korea, November 2000.
166.
T.
Lu and P. N. Suganthan, “The Cumulation Algorithm for Video Shot Detection”, P.
ICARCV ‘2000, Singapore, December 2000.
167.
A.
S. Atukorale and P. N. Suganthan, “Comparing
Performances of Supervised Classifiers”, Proc. Of ICARCV’2000, Singapore,
Dec.
2000.
168.
P.
N. Suganthan, “Attributed Relational Graph Matching by Neural-Gas Networks”, Proc.
Of IEEE Workshop on Neural Networks for Signal Processing, Sydney,
Australia, Dec. 2000.
169.
P.
N. Suganthan, “Solving jigsaw puzzles using Hopfield network”, Proc. IJCNN99,
Washington DC, USA, July 1999.
170.
A. S. Atukorale and P. N.
Suganthan, “Combining multiple HONG networks for recognising unconstrained
handwritten numerals”, In the Proc. Of IJCNN99, Washington DC, USA, July
1999.
171.
P.
N. Suganthan, “Particle
swarm optimisation with a neighbourhood operator”, Proc. Of Congress on
Evolutionary Computation, Washington DC, USA, July 1999.
172.
A.
S. Atukorale and P. N. Suganthan, “Combining
Classifiers Based on Confidence Values”, Proc. Of ICDAR'99
Bangalore, India, September 1999.
173.
A.
S. Atukorale and P. N. Suganthan, “Comparison of
Self-Organizing Maps and Neural Gas Algorithm for Recognizing Unconstrained
Handwritten Numerals”, P. IITC'99, Colombo, Sri Lanka, October 1999.
174.
A.
S. Atukorale and P. N. Suganthan, “Multiple HONG
Network Fusion by Fuzzy Integral”, P. ICONIP'99, Perth,
Australia, November 1999.
175.
P.
N. Suganthan, “Attributed Relational Graph Matching Using Genetic Algorithms”, P.
of ICAPRDT’99, Calcutta, India, December 1999.
176.
P.
N. Suganthan, “Hierarchical self-organising maps”, Proc. of ACNN'98,
Brisbane, February 1998.
177.
P.
N. Suganthan, “Structure adaptive multilayer overlapped SOMs with supervision
for handprinted digit classification”, Proc. of the IJCNN'98, Alaska,
USA, May 1998.
178.
P.
N. Suganthan and N.R.Pal, “Pattern classification using
multiple SOMs”, P. ICONIP'98, Kitakyushu, October 1998.
179.
P.
N. Suganthan, “Hierarchical Overlapped SOM Based Multiple Classifiers
Combination”,
Proc. of the 5th Int. Conf. on Automation, Robotics, Control and Vision,
Singapore, December 1998.
180.
A.
S. Atukorale and P. N. Suganthan, “An efficient
Neural Gas network for classification”, Proc. of the 5th
Int. Conf. on Automation, Robotics, Control and Vision, Singapore,
December 1998.
181.
J.
J. Stoll and P. N. Suganthan, “Face recognition using SOMs”, Proc. of the 5th Int. Conf. on Automation,
Robotics, Control and Vision, Singapore, December 1998.
182.
P.
N. Suganthan and H. Yan, “Stroke-based merged handwritten Kanji characters
recognition”,
Proc. of the Real World Computing Symposium, Japan, January 1997.
183.
P.
N. Suganthan, “Structure adaptive multilayer SOM with partial supervision for
numeral recognition”, Proc of the ICONIP’97, Dunedin, New Zealand,
November 1997.
184.
P.
N. Suganthan and H. Yan, “Handwritten Chinese character recognition by ARG
matching using self-organising Hopfield network”, P. IEEE Int. Conf. on
Neural Networks, Washington DC, June 1996.
185.
P.
N. Suganthan, E. K. Teoh and D. P. Mital, “On mapping
of ARG matching onto neural networks”, P. IEEE Systems, Man and Cybernetics Conference, Canada, October 1995.
186.
P.N.
Suganthan, E. K. Teoh and D. P. Mital, “Homomorphic
graph matching using self-organising Hopfield network”, IEE Artificial
Neural Networks Conference, June 1995, Cambridge, UK.
187.
P.
N. Suganthan, E. K. Teoh and D. P. Mital, “Learning critical
temperature for homomorphic ARG matching by self-organising Hopfield network”,
P. IEEE Int. Conf. on Neural Networks, Perth, Australia, December 1995.
188.
P.
N. Suganthan, E. K. Teoh, D. P. Mital, “Fuzzy
connectives based optimal mapping of homomorphic ARG matching onto
self-organising Hopfield network”, P. IEEE Int. Conf. on Neural Networks,
Perth, Australia, December 1995.
189.
P.
N. Suganthan, E. K. Teoh and D. P. Mital, “Shape
recognition using Potts MFT neural networks”, P. of the Asian Conference on
Computer Vision, Singapore, December 1995.
190.
P.
N. Suganthan, E. K. Teoh and D. P. Mital,
“Programming Hopfield network for relational homomorphism”, P. 1994 IEEE
TENCON, Singapore.
191.
P.
N. Suganthan, E. K. Teoh and D. P. Mital, “On
attributed relational graph matching using Hopfield network”, P. 1994
European Conf. on Artificial Intelligence, The Netherland, August 1994.
192.
P.
N. Suganthan, E. K. Teoh and D. P. Mital,
“Programming Hopfield network for object recognition”, P. of 1993 IEEE Systems, Man and Cybernetics
Conference, France.
193.
P.
N. Suganthan, E. K. Teoh and D. P. Mital, “Flexible
circuits recognition using multilayer back propagation network”, P. of 1993
IEEE IECON Conference, Hawaii.