A Self-Adaptive Evolutionary Multi-Task Based Constrained Multi-Objective Evolutionary Algorithm

作     者：Qiao, Kangjia Liang, Jing Yu, Kunjie Wang, Minghui Qu, Boyang Yue, Caitong Guo, Yinan 

作者机构：Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Peoples R China Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Peoples R China Zhongyuan Univ Technol Sch Elect & Informat Engn Zhengzhou 450007 Peoples R China China Univ Min & Technol Beijing 100084 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE》 (IEEE Trans. Emerging Topics Comp. Intell.)

年 卷 期：2023年第7卷第4期

页      面：1098-1112页

核心收录：

基　　金：National Natural Science Fund for Outstanding Young Scholars of China National Natural Science Foundation of China [62176238, 61806179, 61876169, 61976237] China Postdoctoral ScienceFoundation [2020M682347] Training Program ofYoung Backbone teachers in Colleges and universities in Henan Province [2020GGJS006] Henan Provincial Young Talents Lifting Project [2021HYTP007] 

主　　题：Task analysis Optimization Statistics Sociology Knowledge transfer Multitasking Genetic algorithms Constrained multi-objective optimization evolutionary multi-task optimization knowledge transfer self-adaptive intra-task inter-task 

摘      要：Constrained multi-objective optimization problems (CMOPs) are difficult to solve since they involve the optimization of multiple objectives and the satisfaction of various constraints. Most constrained multi-objective evolutionary algorithms (CMOEAs) are prone to fall into the local optima due to the imbalance between objectives and constraints as well as the poor search ability of the population. To better solve CMOPs, this paper proposes a double-balanced evolutionary multi-task optimization (DBEMTO) algorithm, which evolves two populations to respectively solve the main task (CMOP) and the auxiliary task (MOP extracted from the CMOP). In DBEMTO, three evolutionary strategies are assigned to each population for offspring generation. The three evolutionary strategies include an individual transfer-based inter-task strategy and two intra-task strategies, not only utilizing the information of inter-task but also providing diverse search abilities of intra-task. Moreover, a self-adaptive scheme is developed to self-adaptively employ three strategies, so that the population can balance the information utilization of both intra-task and inter-task. Then, in the environmental selection, the performance of the three strategies is adopted to guide the sharing of the two offspring populations. Compared with several other state-of-the-art CMOEAs, DBEMTO has performed more competitively according to the final results.