A multifactorial differential evolution with hybrid global and local search strategies
Abstract
Evolutionary multitasking optimization (EMTO) solves multiple optimization tasks meanwhile in the framework of evolutionary algorithm, aiming at improving the solving performance on each task via knowledge transfer among tasks. As one of the representative EMTO algorithms, multifactorial evolutionary algorithm (MFEA) has attracted great attention and has been used to solve many optimization problems. However, most of MFEAs tend to suffer from premature convergence. To deal with this issue, this article designs a novel MFEA by integrating differential evolution, and a hybrid of global and local search strategies, named MFDE-GLS for short. Particularly, the global search strategy is based on an opposition-based learning and a Gaussian perturbation to improve the search ability and maintain population diversity. A local search strategy is introduced by combining 1-dimension search and n-dimension search to accelerate the convergence. Moreover, a new environmental selection mechanism is developed to keep the elite individuals while maintaining the population diversity based on the affinity propagation clustering method. Comprehensive experiments were conducted on both single-objective and multi-objective multi-task benchmark problems to show the effectiveness of the proposed algorithm.
Authors: Mingyu Xu, Yongjin Zheng, Yew-Soon Ong, Zexuan Zhu, Xiaoliang Ma
Published in: IEEE Congress on Evolutionary Computation (CEC) (2022)