A Pareto front estimation-based constrained multi-objective evolutionary algorithm

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 fr...

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Vydané v:	Applied intelligence (Dordrecht, Netherlands) Ročník 53; číslo 9; s. 10380 - 10416
Hlavní autori:	Cao, Jie, Yan, Zesen, Chen, Zuohan, Zhang, Jianlin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.05.2023 Springer Nature B.V
Predmet:	Artificial Intelligence Computer Science Convergence Estimation Evolutionary algorithms Feasibility Fitness Genetic algorithms Machines Manufacturing Mechanical Engineering Methods Multiple objective analysis Optimization Pareto optimization Penalty function Populations Processes Constrained multi-objective optimization Multiple-populations Constrained multi-objective evolutionary algorithms Pareto front curvature
ISSN:	0924-669X, 1573-7497
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Abstract	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.
AbstractList	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.
Author	Cao, Jie Zhang, Jianlin Yan, Zesen Chen, Zuohan
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Snippet	The balance of convergence, diversity, and feasibility plays a pivotal role in constrained multi-objective optimization problems. To address this issue, in...
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SubjectTerms	Artificial Intelligence Computer Science Convergence Estimation Evolutionary algorithms Feasibility Fitness Genetic algorithms Machines Manufacturing Mechanical Engineering Methods Multiple objective analysis Optimization Pareto optimization Penalty function Populations Processes
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Title	A Pareto front estimation-based constrained multi-objective evolutionary algorithm
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