Modified Distance-based Subset Selection for Evolutionary Multi-objective Optimization Algorithms

Abstract

Evolutionary algorithms have been widely used to solve multi-objective optimization problems. Usually, the final population of an evolutionary algorithm is used as the output of multi-objective optimization. However, a current new trend is to select a pre-specified number of solutions from an unbounded external archive (UEA) as the final output of multi-objective optimization. Some subset selection methods have been proposed in the literature such as hypervolume-based and IGD-based selection. Recently, a distance-based subset selection (DSS) method was proposed for efficient subset selection from a large external archive. Whereas DSS efficiently finds a set of uniformly distributed solutions, it has some difficulties in the handling of solutions in the UEA as we demonstrate in this paper. To improve the performance of the DSS method, we propose a modified DSS method based on the IGD+ distance instead of the Euclidean distance. Experimental results on various benchmark problems show that the modified DSS method performs better than or equal to the original DSS method on most test problems.