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Parallel strength Pareto multiobjective evolutionary algorithm

Shengwu Xiong, Feng Li

Open publisher page 7 citations

Abstract

A parallel strength Pareto multiobjective evolutionary algorithm (PSPMEA) is proposed. PSPMEA is a parallel computing model designed for solving Pareto-based multiobjective optimization problems by using an evolutionary procedure. In this procedure, both global parallelization and island parallel evolutionary algorithm models are used. Each subpopulation evolves separately with different crossover and mutation probability, but they exchange individuals in the elitist archive. The benchmark problems numerical experiment results demonstrate that the proposed method can rapidly converge to the Pareto optimal front and spread widely along the front.

About this research paper

What this paper is about

A parallel strength Pareto multiobjective evolutionary algorithm (PSPMEA) is proposed. PSPMEA is a parallel computing model designed for solving Pareto-based multiobjective optimization problems by using an evolutionary procedure. In this procedure, both global parallelization and island parallel evolutionary algorithm models are used. Each subpopulation evolves separately with different crossover and mutation probability, but they exchange individuals in the elitist archive. The benchmark problems numerical experiment results demonstrate that the proposed method can rapidly converge to the Pareto optimal front and spread widely along the front.

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OpenAlex reports 7 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

A parallel strength Pareto multiobjective evolutionary algorithm (PSPMEA) is proposed. PSPMEA is a parallel computing model designed for solving Pareto-based multiobjective optimization problems by using an evolutionary procedure. In this procedure, both global parallelization and island parallel evolutionary algorithm models are used. Each subpopulation evolves separately with different crossover and mutation probability, but they exchange individuals in the elitist archive. The benchmark problems numerical experiment results demonstrate that the proposed method can rapidly converge to the Pareto optimal front and spread widely along the front.

Key concepts: Crossover, Benchmark (surveying), Evolutionary algorithm, Multi-objective optimization, Pareto principle, Mathematical optimization, Computer science, Evolutionary computation

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