A modified differential evolution algorithm for multi-objective optimization problems

Differential Evolutionary (DE) is a simple, fast and robust evolutionary algorithm for multi-objective optimization problems (MOPs). This paper is to introduce a modified differential evolutionary algorithm (MDE) to solve MOPs. There are some different points between MDE and traditional DE: individual mutation and its selection strategy; M DE allows infeasible solutions of population to participate in mutation process, and mutation strategy of individuals adapt to a modified updating scheme of particle velocity in PSO. The fast nondominated sorting and ranking selection scheme of NSGA-II proposed by Deb is incorporated into individual's selection process. We finally obtain a set of global optimal solutions (gbest). Simulated experiments show that the obtained solutions present good uniformity of diversity, and they are close to the true frontier of Pareto. Also, the convergence of solutions obtained is satisfactory.