A Study of Distributed Evolutionary Algorithms for Multi-objective Optimisation

Most popular Evolutionary Algorithms for single multi-objective optimisation are motivated by the reduction of the computation time and the resolution larger problems. A promising alternative is to create new distributed schemes that improve the behaviour of the search process of such algorithms. In the multi-objective optimisation problems, more exploration of the search space is required to obtain the whole or the best approximation of the Pareto front. Almost all proposed Parallel Multi-Objective Evolutionary Algorithms (PMOEAs) are based on the specialisation concept which means dividing the objective and/or the search space then assigning each part to a processor. One processor called the organiser or the coordinator is usually charged to direct the whole algorithm. In this paper, we present a new parallel scheme of multi-objective evolutionary algorithms which is based on a clustering technique. This new parallel algorithm is implemented and compared to three PMOEAs which are cone-separation [1], Divided Range Multi-Objective Genetic Algorithm (DRMOGA) [8] and a Parallel Strength Pareto Evolutionary Algorithm (PSPEA) based on the island model without migration.