Special Session & Competition on Real-Parameter Single Objective (Expensive) Optimization at
CEC-2014, Beijing, PR-China 6-11 July 2014
If you face any difficulties, please inform me ( epnsugan@ntu.edu.sg ).
2. 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.
3.
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.
4. To download, Matlab, JAVA, C & Data files. please follow the link: https://github.com/P-N-Suganthan to CEC2014 folder.
5. Comparison of Algorithms and codes of top performing algorithms are available in CEC 2014 folder at this link: https://github.com/P-N-Suganthan
Important Points:
** Solutions should be uniformly initialized within the whole search space.
** Search must be conducted within the defined search ranges. Solutions out side of the defined search ranges are treated as invalid solutions.
Published Papers (Corresponding to TR @ #2)
|
UMOEAs |
Testing
United Multi-Operator Evolutionary Algorithms on the CEC2014 Real-Parameter
Numerical Optimization. By Saber M. Elsayed, Ruhul A. Sarker, Daryl L. Essam and Noha M. Hamza |
|
L-SHADE Winner |
Improving the Search
Performance of SHADE Using Linear Population Size Reduction. By Ryoji Tanabe and Alex S. Fukunaga (Winner of the Competition) |
|
RSDE |
A Differential Evolution
with Replacement Strategy for Real-Parameter Numerical Optimization. By ChangJian Xu, Han Huang, and ShuJin Ye |
|
FERDE |
Memetic Differential
Evolution Based on Fitness Euclidean-Distance Ratio. By B. Y. Qu, J. J. Liang, J. M. Xiao, Z. G. Shang |
|
POBL-ADE |
Partial Opposition-Based
Adaptive Differential Evolution Algorithms: Evaluation on the CEC 2014
Benchmark Set for Real-parameter Optimization. By Zhongyi Hu, Yukun Bao, and Tao Xiong |
|
FCDE |
Differential Evolution
Strategy based on the Constraint of Fitness Values Classification. By Zhihui Li, Zhigang
Shang, B. Y. Qu, J. J. Liang |
|
MVMO |
Evaluating the
Mean-Variance Mapping Optimization on the IEEE-CEC 2014 Test Suite. By István Erlich,
José L. Rueda, and Sebastian Wildenhues |
|
rmalschcma |
Influence of regions on
the memetic algorithm for the CEC’2014 Special Session on Real-Parameter
Single Objective Optimisation. By Daniel Molina Benjamin Lacroix Francisco Herrera |
|
OptBees |
Real-Parameter
Optimization with OptBees. By Renato Dourado Maia, Leandro Nunes de Castro, and Walmir
Matos Caminhas |
|
SOO |
Bandits attack function
optimization. By Philippe Preux
and R´emi Munos and
Michal Valko |
|
SOO+BOBYQA |
The same as above. |
|
FWA-DE |
Fireworks Algorithm with
Differential Mutation for Solving the CEC 2014 Competition Problems. By
Chao Yu, Lingchen Kelley, Shaoqiu Zheng, and Ying Tan |
|
CMLSP |
An Evolutionary Algorithm
Based on Covariance Matrix Leaning and Searching Preference for Solving CEC
2014 Benchmark Problems. By Lei Chen, Hai-Lin Liu, Zhe Zheng,
Shengli Xie |
|
GaAPADE |
Gaussian Adaptation based
Parameter Adaptation for Differential Evolution. By R. Mallipeddi, Guohua Wu, Minho Lee and P. N. Suganthan |
|
NRGA |
Non-Uniform Mapping in
Real-Coded Genetic Algorithms. By Dhebar Yashesh, Kalyanmoy Deb and Sunith
Bandaru |
|
b3e3pbest |
Differential Evolution
with Rotation-Invariant Mutation and Competing-Strategies Adaptation.
By Petr Bujok, Josef Tvrdık and
Radka Polakov |
|
DE_b6e6rl
with restart |
Controlled Restart in
Differential Evolution Applied to CEC2014 Benchmark Functions. By
Radka Polakova, Josef Tvrdık and
Petr Bujok |
Published Papers (Corresponding to TR @ #3)
|
WA (available as a technical report) |
An Approach to Solve Computationally Expensive Optimization
Problems of CEC‐2014 Without Approximation.
By Md Asafuddoula, Tapabrata
Ray |
|
SA‐DE‐DPS |
A
Surrogate‐SA DE assisted Differential
Evolution algorithm with Dynamic Parameters Selection for Solving Expensive
Optimization Problems. By
SaberM. Elsayed, T. Ray, Ruhul A. Sarker |
|
HSBA |
A
Hybrid Surrogate based Algorithm (HSBA) to Solve Computationally Expensive
Optimization Problems. By
Hemant Kumar Singh, Amitay
Isaacs, Tapabrata Ray |
|
GCO |
Evaluating the performance of Group Counselling Optimizer on CEC
2014 problems for Computational Expensive Optimization. By Subhodip Biswas,
Mohammad A. Eita, Swagatam Das, Athanasios V. Vasilakos |
|
MVMO (Winner) |
Solving the IEEE CEC 2014 Expensive Optimization Test Problems
by Using Single‐Particle MVMO. By István Erlich,
José L. Rueda, SebastianWildenhues |
|
SOMODS |
SO‐MODS: Optimization for High Dimensional Computationally
Expensive Multi‐Modal Functions with
Surrogate Search.
By Tipaluck Krityakierne, Juliane
Müller, Christine A. Shoemaker |
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Paper Submission
Authors must strictly follow the manuscript preparation instructions: http://www.ieee-wcci2014.org/Paper-Submission.htm
When submitting, please make sure you select "SS42. EC42: Single Objective Numerical Optimization" as the "Main Research Topic" for all papers making use of these benchmarks. Each submission should include either the standard or expensive problems (i.e. these 2 sets of problems should not be combined into one paper).
Paper Submission Deadline
We will follow the extended deadlines as determined by WCCI 2014.
Request for Feedback
If you have suggestions to improve the technical report or if you find a potential bug in the codes, please inform us.
Software packages (Matlab, JAVA, C) & data files of the single objective optimization were updated on 23rd of Dec.
Software packages of expensive (or reduced fitness evaluations based) single objective optimization were updated on 30th of Dec.