Evolutionary Multitasking with Global and Local Auxiliary Tasks for Constrained Multi-Objective Optimization

人类多任务处理 计算机科学 水准点(测量) 人口 任务(项目管理) 局部搜索(优化) 进化算法 多目标优化 数学优化 帕累托原理 最优化问题 人工智能 机器学习 算法 数学 工程类 社会学 人口学 认知心理学 系统工程 地理 心理学 大地测量学
作者
Kangjia Qiao,Jing Liang,Zhongyao Liu,Kunjie Yu,Caitong Yue,Boyang Qu
出处
期刊:IEEE/CAA Journal of Automatica Sinica [Institute of Electrical and Electronics Engineers]
卷期号:10 (10): 1951-1964 被引量:43
标识
DOI:10.1109/jas.2023.123336
摘要

Constrained multi-objective optimization problems (CMOPs) include the optimization of objective functions and the satisfaction of constraint conditions, which challenge the solvers. To solve CMOPs, constrained multi-objective evolutionary algorithms (CMOEAs) have been developed. However, most of them tend to converge into local areas due to the loss of diversity. Evolutionary multitasking (EMT) is new model of solving complex optimization problems, through the knowledge transfer between the source task and other related tasks. Inspired by EMT, this paper develops a new EMT-based CMOEA to solve CMOPs, in which the main task, a global auxiliary task, and a local auxiliary task are created and optimized by one specific population respectively. The main task focuses on finding the feasible Pareto front (PF), and global and local auxiliary tasks are used to respectively enhance global and local diversity. Moreover, the global auxiliary task is used to implement the global search by ignoring constraints, so as to help the population of the main task pass through infeasible obstacles. The local auxiliary task is used to provide local diversity around the population of the main task, so as to exploit promising regions. Through the knowledge transfer among the three tasks, the search ability of the population of the main task will be significantly improved. Compared with other state-of-the-art CMOEAs, the experimental results on three benchmark test suites demonstrate the superior or competitive performance of the proposed CMOEA.
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