Special Session & Competition on Real-Parameter Single Objective Optimization (3 Different Cases)
at CEC-2015, Sendai International Centre, Sendai, Japan, 25-28 May 2015
If you face any difficulties, please inform me ( epnsugan@ntu.edu.sg ).
https://github.com/P-N-Suganthan
2. 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.
3.
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.
4.
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.
[ <-- We request the usage of the new reference.
Old
Reference: B. Y. Qu, J. J. Liang, P. N. Suganthan,
Q. Chen, "Problem Definitions and Evaluation Criteria for the CEC
2015 Competition on Single Objective Multi-Niche Optimization", Technical Report, Computational
Intelligence Laboratory, Zhengzhou University, Zhengzhou, China
and Technical Report, Nanyang Technological University, Singapore,
Nov 2014. (Final
Solution Submission Format is Given Here) ]
Comparison slides for
all 3 cases are available in the respective folders: https://github.com/P-N-Suganthan
Learning Based Papers
|
Paper ID |
Algorithm |
Title |
|
|
15031 |
SPS-L-SHADE-EIG |
A Self-Optimization Approach for L-SHADE
Incorporated with Eigenvector-Based Crossover and Successful-Parent-Selecting
Framework on CEC 2015 Benchmark Set. Rank - #1 |
|
|
15096 |
TEBO |
Tuning Maturity Model of Ecogeography-Based
Optimization On CEC 2015 Single-Objective Optimization Test Problems |
|
|
15170 |
MVMO |
Testing MVMO on Learning-based Real-Parameter Single
Objective Benchmark Optimization Problems Rank - #3 |
|
|
15230 |
LSHADE-ND |
Neurodynamic Differential Evolution Algorithm and Solving
CEC2015 Competition Problems Rank - #3 |
|
|
15287 |
ICMLSP |
An Improved Covariance Matrix Leaning and Searching
Preference Algorithm for Solving CEC 2015 Benchmark Problems |
|
|
15460 |
SaDPSO |
A Self-adaptive Dynamic Particle Swarm Optimizer |
|
|
15473 |
cooperation |
Cooperation of Optimization Algorithms: A Simple
Hierarchical Model |
|
|
15485 |
hCC |
Hybrid Cooperative Co-evolution For The CEC15
Benchmarks |
|
|
15527 |
ABC-X-LS |
A Configurable Generalized Artificial Bee Colony
Algorithm with Local Search Strategies |
|
|
15598 |
dynFWA |
Dynamic Search Fireworks Algorithm for Solving
CEC2015 Competition Problems |
|
|
15620 |
DEsPA |
A Differential Evolution Algorithm with Successbased Parameter Adaptation for CEC2015 Learning
based Optimization Rank - #2 |
|
|
15642 |
dynFWACM |
Dynamic Search Fireworks Algorithm with Covariance
Mutation for Solving the CEC 2015 Learning Based Competition Problems |
|
|
15667 |
HumanCog |
HumanCog: A Cognitive Architecture for Solving Optimization
Problems |
|
Expensive Optimization Papers
|
Paper ID |
Algorithm |
Score / Rank |
|
E-15035 |
MVMO |
3,062,550.15 |
|
E-15487 |
TunedCMAES |
203,324,192.51 |
|
E-15664 |
CMAS-ES_QR |
475,807,278.19 |
|
E-15667 |
iSRPSO |
9,213,589,132.86 |
|
E-15682 |
humanCog |
106,093,535,263.79 |
Multi-Niche optimization Papers
#15611: An evolutionary algorithm based on decomposition for multimodal optimization problems. (Rank 1)
#15568: Multimodal optimization using particle swarm optimization algorithms: CEC 2015 competition on single objective multi-niche optimization. (Rank 2)
#15629: Solving CEC 2015 Multimodal competition problems using neighborhood based speciation differential evolution. (Rank 3)
Paper Submission
Authors must strictly follow the manuscript preparation instructions: Available Here
When submitting, please make sure you select "SS04: Single Objective Numerical Optimization" as the "Main Research Topic" for all papers making use of these benchmarks. Each submission should have either the learning-based or expensive or multiple-niche problems (i.e. these 3 sets of problems should not be combined into a single paper).
Paper Submission Deadline
We will follow
the extended deadlines as determined by CEC
2015. (Currently 19th of Dec 2014). If you're unable to meet the final extended
deadline, please inform us ( epnsugan@ntu.edu.sg ).
#2 on 7th Jan 2015
Niching Problems: (updated on 7th Jan 2015)
Codes:
1. Test function 5, there is a bug in the code and will affect the result.
Three versions have been updated.
2. Test function 7, the accuracy is increased, the former "4.1265" in
the function which is used to make f(x*)=0 is updated
to "4.126514".
TR:
1. Errors in test functions 6, 7 and 8 are corrected.
2. pages are inserted.
#1 on 3rd Jan 2015
Learning-Based Problems：(updated on 3rd Jan 2015)
lamada definded for "Rotated
Expanded Scaffer’s F6 Function" in the
composition function is modified since the previous one is too small to make
the whole subregion has a low function
values. 5e-4 is modified to
10. Affected functions:
page 15: (12) Composition Function 4
page 16: (13) Composition Function 5
page 17: (14) Composition Function 6
page 18: (15) Composition Function 7
The codes are all updated and authors do not have to correct the results now, but for the final paper, they should rerun these four functions.
Niching Problems: (updated on 3rd Jan 2015)
1. 11)Composition Function 3 bias= [0, 10, 20, 30, 40, 50, 60, 70, 80, 90] is corrected to bias = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] +F11*. The codes are correct. So, not to be modified.
2. lamada definded for "Rotated Expanded Scaffer’s F6 Function" in the composition function is modified to "100" since the previous one is too small to make the whole subregion to have low function values. Affected functions:
11)
Composition Function 3
12) Composition Function 4
13) Composition Function 5
15) Composition Function 7
The codes are all updated and authors do not have to correct the results now, but for the final paper, they should rerun these four functions.
Request for Feedback
If you have suggestions to improve the technical report or if you find any potential bug in the codes, please inform us ( epnsugan@ntu.edu.sg ).