Partitioned parallelization of MOEA/D for bi-objective optimization on clusters

Abstract

The multi-objective evolutionary algorithm based on decomposition (MOEA/D) has a remarkable overall performance for multi-objective optimization problems, but still consumes much time when solving complicated problems. A parallel MOEA/D (pMOEA/D) is proposed to solve bi-objective optimization problems on message-passing clusters more efficiently in this paper. The population is partitioned evenly over processors on a cluster by a partitioned island model. Besides, the sub-populations cooperate among separate processors on the cluster by the hybrid migration of both elitist individuals and utopian points. Experimental results on five bi-objective benchmark problems demonstrate that pMOEA/D achieves the satisfactory overall performance in terms of both speedup and quality of solutions on message-passing clusters.