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A Pareto front estimation-based constrained multi-objective evolutionary algorithm
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AI
DOI：10.1007/s10489-022-03990-7.png)
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Abstract
En 中文
The balance of convergence, diversity, and feasibility plays a pivotal role in constrained multi-objective optimization problems. To address this issue, in this paper a novel method named PeCMOEA is proposed, in which the pivotal solutions, which are designed for estimating the constrained Pareto front, are identified through an achievement scalarizing function. In addition, two different adaptive fitness functions are formulated to evaluate convergence- and diversity-oriented populations, respectively. Finally, the promising solutions from the two populations are reserved by their fitness values in the environmental selection while a self-adaptive penalty function is designed to repair infeasible solutions and ensure their feasibility. The performance of PeCMOEA is compared with five state-of-the-art constrained multi-objective evolutionary algorithms on five test suites. The experimental results illustrate that PeCMOEA exhibits competitive performance when utilised for this family of problems.
Keywords:
Multiple-populations
Constrained multi-objective optimization
Constrained multi-objective evolutionary algorithms
Pareto front curvature
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