Evolutionary Multi-tasking Single-Objective Optimization Based on Cooperative Co-evolutionary Memetic Algorithm

Evolutionary multi-tasking optimization has recently emerged as a promising new topic in the field of evolutionary computation. It is a promising framework for solving different optimization problems simultaneously. Compared with the classic evolutionary algorithms, evolutionary multi-tasking optimization (MTO) can take advantage of implicit genetic transfer in the optimization process and get better performance. Distinct tasks are solved simultaneously by utilizing similarities and differences across different tasks. In this paper, an evolutionary multi-tasking single-objective optimization based on cooperative co-evolutionary memetic algorithm (EMTSO-CCMA) is proposed. A local search method based on quasi-Newton is proposed to accelerate the convergence of the proposed algorithm. The effectiveness of the proposed algorithm is shown in this paper by comparing with the multifactorial evolutionary algorithm.