Adaptive Memetic Algorithm Based Evolutionary Multi-tasking Single-Objective Optimization

摘要

Evolutionary multitasking optimization has recently emerged as an effective framework to solve different optimization problems simultaneously. Different from the classic evolutionary algorithms, multi-task optimization (MTO) is designed to take advantage of implicit genetic transfer in a multitasking environment. It deals with multiple tasks simultaneously by leveraging similarities and differences across different tasks. However, MTO still suffers from a few issues. In this paper, a multifactorial memetic algorithm is introduced to solve the single-objective MTO problems. Particularly, the proposed algorithm introduces a local search method based on quasi-Newton, reinitializes a port of worse individuals, and suggests a self-adapt parent selection strategy. The effectiveness of the proposed algorithm is validated by comparing with the multifactorial evolutionary algorithm proposed in CEC'17 competition.