PSFGA: a parallel genetic algorithm for multiobjective optimization

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.