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2003•Wuhan University JournalRequires access

Parallel Pareto Multi-Objective Evolutionary Algorithm

Feng Li

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Abstract

A Parallel Pareto Multi-objective Evolutionary Algorithm(PPMEA) is proposed. PPMEA is a parallel computing model designed for solving Pareto-based multi-objective 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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What this paper is about

A Parallel Pareto Multi-objective Evolutionary Algorithm(PPMEA) is proposed. PPMEA is a parallel computing model designed for solving Pareto-based multi-objective 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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Available abstract

A Parallel Pareto Multi-objective Evolutionary Algorithm(PPMEA) is proposed. PPMEA is a parallel computing model designed for solving Pareto-based multi-objective 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: Benchmark (surveying), Crossover, Evolutionary algorithm, Pareto principle, Multi-objective optimization, Mathematical optimization, Computer science, Algorithm

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