PSFGA: parallel processing and evolutionary computation for multiobjective optimisation
Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
Parallel implementation of evolutionary strategies on heterogeneous clusters with load balancing
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
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In this paper a performance analysis in a cluster system of the Parallel Single Front Genetic Algorithm (PSFGA) is carried out. The PSFGA is a parallel evolutionary optimizer for multiobjective problems that use a structured population in the form of a set of islands. The SFGA, an elitist evolutionary algorithm with a clearing procedure that uses a grid in the objective space for diversity maintaining purposes, is performed on each subpopulation (island) associated to a different area in the search space. Experimental results show that PSFGA outperforms SFGA and SPEA (Strength Pareto Evolutionary Algorithm) in the cases studied.