PSFGA:多目标优化的并行遗传算法

Proceedings 10th Euromicro Workshop on Parallel, Distributed and Network-based Processing Pub Date : 2002-01-09 DOI:10.1109/EMPDP.2002.994315

F. D. Toro, J. Ortega, Javier Fernández, A. F. Díaz

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引用次数: 58

摘要

提出了并行单前端遗传算法(PSFGA)，这是一种基于进化过程的并行pareto优化算法。在此过程中，将解的总体根据目标函数的值进行排序，并将其划分为分布在处理器之间的子总体。每个处理器将我们设计的顺序多目标遗传算法(称为单前端遗传算法，SFGA)应用于其子种群。实验结果将PSFGA算法与已有的多目标进化算法进行了比较。

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PSFGA: a parallel genetic algorithm for multiobjective optimization

This paper presents the parallel single front genetic algorithm (PSFGA), a parallel Pareto-based algorithm for multiobjective optimization problems based on an evolutionary procedure. In this procedure, a population of solutions is sorted with respect to the values of the objective functions and partitioned into subpopulations which are distributed among the processors. Each processor applies a sequential multiobjective genetic algorithm that we have devised (called single front genetic algorithm, SFGA) to its subpopulation. Experimental results are provided comparing PSFGA with previously proposed multiobjective evolutionary algorithms.

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Proceedings 10th Euromicro Workshop on Parallel, Distributed and Network-based Processing

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