Abstract
Evolutionary multitasking aims to explore implicit synergy among multiple optimization tasks. Through the effect of hitchhiking, evolutionary multitasking is capable of improving the performance of evolutionary algorithms on exploration as well as exploitation. Multifactorial evolutionary algorithm (MFEA) presented an effectual implementation of evolutionary multitasking, which simultaneously seeks the solutions to multiple optimization problems by unifying their search spaces. The MFEA enables information sharing across tasks during evolution. This mechanism can improve the evolutionary efficiency in the early phase; however, it will impair the exploitation and consume extra resources later on, due to the essential difference among the fitness landscapes of optimization problems. This study proposes detecting the occurrence of parting ways, at which the information sharing begins to fail. In addition, we develop the resource allocation mechanism to reallocate the fitness evaluations on different types of offspring by ceasing information sharing when parting ways. Experiments are conducted to evaluate the proposed methods. The experimental results show that applying parting ways detection and resource reallocation for MFEA can achieve better solution quality in most of testing cases, especially when the tasks share low similarity of landscapes.