A comparative study of constrained multi-objective evolutionary algorithms on constrained multi-objective optimization problems

Solving constrained multi-objective optimization problems is a difficult task, it needs to simultaneously optimize multiple conflicting objectives and a number of constraints. This paper first reviews a number of popular constrained multi-objective evolutionary algorithms (CMOEAs) and twenty-three w...

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Bibliographic Details
Published in:2017 IEEE Congress on Evolutionary Computation (CEC) pp. 209 - 216
Main Authors: Zhun Fan, Yi Fang, Wenji Li, Jiewei Lu, Xinye Cai, Caimin Wei
Format: Conference Proceeding
Language:English
Japanese
Published: IEEE 01.06.2017
Subjects:
Algorithm design and analysis
Evolutionary computation
Pareto optimization
Sociology
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Summary:Solving constrained multi-objective optimization problems is a difficult task, it needs to simultaneously optimize multiple conflicting objectives and a number of constraints. This paper first reviews a number of popular constrained multi-objective evolutionary algorithms (CMOEAs) and twenty-three widely used constrained multi-objective optimization problems (CMOPs) (including CF1-10, CTP1-8, BNH, CONSTR, OSY, SRN and TNK problems). Then eight popular CMOEAs with simulated binary crossover (SBX) and differential evolution (DE) operators are selected to test their performance on the twenty-three CMOPs. The eight CMOEAs can be classified into domination-based CMOEAs (including ATM, IDEA, NSGA-II-CDP and SP) and decomposition-based CMOEAs (including CMOEA/D, MOEA/D-CDP, MOEA/D-SR and MOEA/D-IEpsilon). The comprehensive experimental results indicate that IDEA has the best performance in the domination-based CMOEAs and MOEA/D-IEpsilon has the best performance in the decomposition-based CMOEAs. Among the eight CMOEAs, MOEA/D-IEpsilon with both SBX and DE operators has the best performance on the twenty-three test problems.
DOI:10.1109/CEC.2017.7969315