Effects of Parent Selection Schemes from Other Tasks on the Search Performance of Evolutionary Multiobjective Multitasking

Abstract

Recently, evolutionary multitasking that solves multiple optimization problems (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 evolutionary multitasking is that parents are selected from populations for not only its own task but also another. Our previous study showed that the search performance of evolutionary multitasking is improved by appropriately setting the frequency of crossover between different populations. However, there is no study about the effect of parent selection schemes from other tasks. Thus, selection of appropriate parents from multiple populations is not well-studied. In this paper, we focus on the similarity among individuals in the decision variable space and examine the effects of different parent selection schemes from other tasks on the search performance of evolutionary multiobjective multitasking.