Research on an Orthogonal and Model Based Multi-objective Genetic Algorithm


Against low efficiency of traditional multi-objective evolutionary algorithms and poor utilization of Pareto-optimal solutions distribution regularity etc, in this paper, a new approach OMEA is proposed. It uses that distribution regularity to obtain good solutions, we also apply the orthogonal design to initialize population. Compared with SPEA2, NSGA-II and PAES, Pareto solutions by OMEA are closer to Pareto-optimal Front. The result of experiments shows a group of Pareto solutions with better convergence and diversity can be achieved, which gives strong supports to actual applications.