Multi-Objective Evolutionary Algorithm Based on Max-Min Distance Density

Abstract

This paper proposed a multi-objective differential evolution algorithm based on max-min distance density. The algorithm proposed the definiteness of max-min distance density and a Pareto candidate solution set maintenance method, and ensured the diversity of the Pareto solution set. Using Pareto dominance relationship among individuals and max-min distance density ensured the convergence of the algorithm, realized solving multi-objective optimization problems. The proposed algorithm is applied to five ZDT test functions and compared with others multi-objective evolutionary algorithms. Experimental result and analysis show that the algorithm is feasible and efficient