Self-Adjusting Multitask Particle Swarm Optimization

Abstract

Particle swarm optimization algorithm has become a promising approach in solving multitask optimization (MTO) problems since it can transfer knowledge with easy implementation and high searching efficiency. However, in the process of knowledge transfer, negative transfer is common because it is difficult to evaluate whether knowledge is effective for population evolution. Therefore, how to obtain and transfer the effective knowledge to curb the negative transfer is a challenging problem in MTO. To deal with this problem, a self-adjusting multitask particle swarm optimization (SA-MTPSO) algorithm is designed to improve the convergence performance in this article. First, a knowledge estimation metric, combining the decision space knowledge and the target space knowledge for each task, is designed to describe the effectiveness of knowledge. Then, the effective knowledge is obtained to promote the knowledge transfer process. Second, a self-adjusting knowledge transfer mechanism, based on the effective knowledge and the self-adjusting transfer method, is developed to achieve effective knowledge transfer. Then, the ineffective knowledge is removed to solve the negative transfer problem. Third, the convergence analysis is given to guarantee the effectiveness of the SA-MTPSO algorithm theoretically. Finally, the proposed algorithm is compared with some existing MTO algorithms. The results show that the performance of the proposed algorithm is superior to most algorithms on negative transfer suppression and convergence.