Improvement of an Iterative EMO Approach based on the Hypervolume Measure

Abstract

The search ability of different evolutionary multiobjective optimization algorithms is often compared using evaluation measures of obtained non-dominated solution sets. The hypervolume measure is one of the most frequently used evaluation measures in the literature. Recently several algorithms have been proposed to directly maximize the hypervolume measure. Those algorithms are often called indicator-based evolutionary algorithms (IBEAs). In our former study, we proposed an iterative IBEA where a single solution is obtained from its single run. In each run, our iterative IBEA searches for a solution with the maximum contribution to the hypervolume measure. In this paper, we slightly modify it to improve its search ability. In the modified version, first our iterative IBEA searches for an optimal solution of each objective. We do not use the hypervolume measure in this stage. After an optimal or near optimal solution of each objective is obtained, our iterative IBEA searches for a solution with the maximum contribution to the hypervolume in its each run. Through computational experiments on multiobjective 0/1 knapsack problems, we demonstrate that the simple modification significantly improves the search ability of our iterative IBEA.