Differential evolution mutation operators for constrained multi-objective optimization

•Infeasible solutions are randomized, instead of undergoing DE mutation. The operator can balance major mutation and minor mutation.•The DE mutation operator in feasible solutions is modified. The novel mechanism can balance convergence and diversity.•The experimental results have indicated that the...

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Veröffentlicht in:Applied soft computing Jg. 67; S. 452 - 466
Hauptverfasser: Yu, Xiaobing, Yu, Xianrui, Lu, Yiqun, Yen, Gary G., Cai, Mei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.06.2018
Schlagworte:
Constraints handling techniques
Differential evolution
Evolutionary algorithm
Multi-objective
Differential evolution
Multi-objective
Evolutionary algorithm
Constraints handling techniques
ISSN:1568-4946, 1872-9681
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Zusammenfassung:•Infeasible solutions are randomized, instead of undergoing DE mutation. The operator can balance major mutation and minor mutation.•The DE mutation operator in feasible solutions is modified. The novel mechanism can balance convergence and diversity.•The experimental results have indicated that the proposed DE algorithm is competitive compared with evolutionary algorithms. Many real-world optimization problems belong to constrained multi-objective optimization problems (CMOPs). Handling constraints and optimizing objectives are two equally important goals. With effective and efficient population-based meta-heuristics in mind, how to generate the offspring with good convergence and diversity properties is a problem to be solved. Competitive algorithms based on different evolution (DE) metaphors have been proposed to solve CMOPs over years as the performance of the DE is attractive. The creative idea of the proposed algorithm is to design a novel mutation mechanism for handling infeasible solutions and feasible solutions respectively. The mechanism can produce well distributed Pareto optimal front while satisfying all concerning constraints. The performance of the algorithm is evaluated on nineteen benchmark functions. Compared with three representative constraint handling techniques and latest optimization algorithms, experimental results have indicated that the proposed algorithm is an effective candidate for real-world problems. At last, the proposed method is used to solve combined economic emission dispatch (CEED) problem. The experiment results have further validated the efficiency of the method.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2018.03.028