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A constrained multi-objective optimization algorithm with two cooperative populations
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AI
DOI：10.1007/s12293-022-00360-1.png)
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Abstract
En 中文
Constrained multi-objective problems (CMOPs) require balancing convergence, diversity, and feasibility of solutions. Unfortunately, the existing constrained multi-objective optimization algorithms (CMOEAs) exhibit poor performance when solving the CMOPs with complex feasible regions. To solve this shortcoming, this work proposes an improved algorithm named the CMOEA-TCP, which maintains two populations cooperating to push the solutions to approximate the constrained Pareto front. Specifically, one population is obtained by the Pareto-based method and aims to strengthen the algorithm's convergence ability. Meanwhile, another population is maintained by decomposition-based method and devoted to improving its diversity. The two populations work cooperatively during the entire evolution process with the constraint-handling technique. The performance of the CMOEA- TCP is verified on three benchmark suites with 34 problems. The experimental results demonstrate that the CMOEA-TCP can achieve performance comparable to or better than the other six state-of-the-art CMOEAs on the majority of considered problems.
Keywords:
Constrained multi-objective optimization problem
Constrained multi-objective algorithm
Cooperative population
Constraint-handling technique
Journal
IF:
2.3
Papers:
453
Citations:
718