Behavior of Bioinspired Algorithms in Parallel Island Models

Abstract

Parallel island models are used to increase accuracy and performance (speed-up) of meta-heuristics. Such models provide gains by the exchange of information between islands through the migratory process. The key to obtaining gains with parallel island models is the manipulation of migration parameters, since depending on how these parameters are handled the gains vary. Based on this assumption, this work uses three meta-heuristics: genetic algorithm, self-adjusting particle swarm optimization and social spider algorithm. From each metaheuristic, parallel island models were proposed, diversifying the number of natives on the islands, and the behavior of these models were studied. The assessment confirmed the impact of variations migration parameters on accuracy and performance as well as the importance on the number of natives located on the islands. The best solutions were obtained with island models from genetic algorithm and self-adjusting particle swarm optimization, and the best speedups were achieved with island models from social spider algorithm.