Multiobjective Differential Evolution with External Archive and Harmonic Distance-Based Diversity Measure

This paper presents an approach to incorporate Pareto dominance into the differential evolution (DE) algorithm in order to solve optimization problems with more than one objective by using the DE algorithm. Unlike the existing proposals to extend the DE to solve multiobjective optimization problems, our algorithm uses an external archive to store nondominated solutions. In order to generate trial vectors, the current population and the nondominated solutions stored in the external archive are used. We also propose a new harmonic average distance to measure the crowding degree of the solutions more accurately. Simulation results on nine test problems show that the proposed MODE, in most problems, is able to find much better spread of solutions with better approximating the true Pareto-optimal front compared to three other multiobjective optimization evolutionary algorithms. Further the new crowding degree estimation method improves the diversity of the nondominated solutions along the Pareto front.