Pareto-based Multi-Objective Differential Evolution


Evolutionary multi-objective optimization (EMOO) finds a set of Pareto solutions rather than any single aggregated optimal solution for a multi-objective problem. The purpose of this paper is to describe a newly developed evolutionary approach --- Pareto-based multi-objective differential evolution (MODE). In this paper, the concept of differential evolution, which is well-known in the continuous single-objective domain for its fast convergence and adaptive parameter setting, is extended to the multi-objective problem domain. A Pareto-based approach is proposed to implement the differential vectors. A set of benchmark test functions is used to validate this new approach. We compare the computational results with those obtained in the literature, specifically by strength Pareto evolutionary algorithm (SPEA). It is shown that this new approach tends to be more effective in finding the Pareto front in the sense of accuracy and approximate representation of the real Pareto front with comparable efficiency.