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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| Published in: | Swarm and evolutionary computation Vol. 66; p. 100940 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01.10.2021
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| Subjects: | |
| ISSN: | 2210-6502 |
| Online Access: | Get full text |
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| Summary: | •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. |
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| ISSN: | 2210-6502 |
| DOI: | 10.1016/j.swevo.2021.100940 |