PSFGA: a parallel genetic algorithm for multiobjective optimization

Authors:
Francisco de Toro;Julio Ortega;Javier Fernández;Antonio Díaz
Affiliations:
University of Huelva, Spain;University of Granada, Spain;University of Granada, Spain;University of Granada, Spain
Venue:
EUROMICRO-PDP'02 Proceedings of the 10th Euromicro conference on Parallel, distributed and network-based processing
Year:
2002

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Abstract

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.