Paper Info
Title
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.
Year
DOI
Venue
2022
10.1109/TEVC.2021.3098523
IEEE Transactions on Evolutionary Computation
Keywords
DocType
Volume
Knowledge estimation,multitask particle swarm optimization (MTPSO),negative transfer,self-adjusting
Journal
26
Issue
ISSN
Citations 
1
1089-778X
0
PageRank 
References 
Authors
0.34
2
5
Name
Order
Citations
PageRank
Hong-Gui Han147639.06
Xing Bai200.34
Huayun Han300.34
Ying Hou4403.43
Jun-Fei Qiao579874.56