LAUSR

This paper presents a universal partially evolved parallelization of the multi-objective evolutionary algorithm based on decomposition (MOEA/D) for multi-objective optimization on message-passing clusters to reduce its computation time. The partially evolved MOEA/D (peMOEA/D) is suitable not only for the bi-objective space, but also for higher dimensional objective spaces by using a partially evolved island model. This model improves the algorithm universality and population diversity by keeping a subpopulation equal in size to the entire population, but only evolving a partial (equal to a partition size) subpopulation on each separate processor in a cluster. Furthermore, a nearest-neighbor partitioning approach, hybrid migration policy and adaptive neighbor-based topology are adopted in the peMOEA/D. The fat partitions generated by the nearest-neighbor partitioning can reduce migration traffic of elitist individuals across subpopulations. Then, hybrid migration of both elitist individuals and utopian points helps separate subpopulations to cooperate in guiding the search quickly towards a complete front. Next, the adaptive neighbor-based topology connects neighbor partitions only and achieves an excellent balance between convergence speed and migration traffic. Experimental results on benchmark multi-objective optimization problems with two or more objectives demonstrate the satisfactory overall performance of the peMOEA/D in terms of both convergence performance and speedup on message-passing clusters.