Effects of Parent Selection in Inter-Task Crossover on the Search Ability of Evolutionary Multiobjective Multitasking

Abstract

Recently, evolutionary multiobjective multitasking (EMOMT) that solves multiple multiobjective optimization problems (i.e., multiple tasks) in parallel using evolutionary computation has been actively studied. In evolutionary multitasking, each task has a population to be optimized by evolutionary computation. The main feature of EMOMT is that offspring individuals are generated by not only intra-task crossover but also inter-task crossover. Previous studies show that adding offspring individuals generated by inter-task crossover to each population improves the search ability of EMOMT. In inter-task crossover, the position of parent individuals in the decision space affects the generated offspring individuals and the next population. For example, when the position of parent individuals is extremely far away in the decision space, the generation area of offspring individuals becomes very large. However, selection of appropriate parents in inter-task crossover is not well-studied. In this paper, we focus on the similarity of individuals between two populations (i.e., two tasks) in the decision space and examine the effects of different parent selection schemes on the search performance of EMOMT.