A partition-based constrained multi-objective evolutionary algorithm

•The CMOP is divided into a series of sub-problems by objective space partition.•A hybrid constrained handling technique including the unconstrained search mode.•A selection operator based on partition feasible solutions.•Mating-pool selection based on population and external archive. Solving constr...

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Vydané v:Swarm and evolutionary computation Ročník 66; s. 100940
Hlavní autori: Yang, Yongkuan, Liu, Jianchang, Tan, Shubin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.10.2021
Predmet:
Compressed-air pipeline optimization
Constrained multi-objective optimization
Differential evolution
Partition selection
Constrained multi-objective optimization
Differential evolution
Compressed-air pipeline optimization
Partition selection
ISSN:2210-6502
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Shrnutí:•The CMOP is divided into a series of sub-problems by objective space partition.•A hybrid constrained handling technique including the unconstrained search mode.•A selection operator based on partition feasible solutions.•Mating-pool selection based on population and external archive. Solving constrained multi-objective optimization problems (CMOPs) is full of challenges due to the difficulties in balancing between feasibility, convergence and distribution. To remedy this issue, this paper proposes a multi-objective differential evolutionary algorithm based on partition selection (MODE-PS). Firstly, MODE-PS divides a CMOP into a series of optimization sub-problems by objective space partition to maintain the distribution. Then, to keep the feasibility of the subspaces, one feasible solution of each subspace is saved to a partition feasible solution set. Next, once there are feasible solutions in one subspace, the individual selection strategy of this subspace is changed from constraint search to non-constraint search. By this way, the convergence is accelerated. Finally, all the feasible solutions are archived and evolved together with the population by a mating-pool selection to balance the feasibility, convergence and distribution. Twenty-two benchmark test problems are used to test the performance of MODE-PS in comparison with five state-of-the-art constrained multi-objective evolution algorithms. Moreover, a real-world problem, i.e., bi-source compressed-air pipeline optimization, is used to test the performance of algorithms. The experimental results have demonstrated the high competitiveness of MODE-PS for solving CMOPs.
ISSN:2210-6502
DOI:10.1016/j.swevo.2021.100940