AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking

作     者：Osaba, Eneko Del Ser, Javier Martinez, Aritz D. Lobo, Jesus L. Herrera, Francisco 

作者机构：TECNALIA Basque Res & Technol Alliance BRTA Derio 48160 Bizkaia Spain Basque Ctr Appl Math BCAM Bilbao 48009 Spain Univ Granada DaSCI Andalusian Inst Data Sci & Computat Intelli Granada Spain 

出 版 物：《INFORMATION SCIENCES》 (信息科学)

年 卷 期：2021年第570卷

页      面：577-598页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Basque Government [IT1294-19] ELKARTEK program (3KIA project) [KK-2020/00049] project SCOTT: Secure Connected Trustable Things (ECSEL Joint Undertaking) Spanish Government [TIN2017-89517-P] 

主　　题：Multitasking Transfer optimization Evolutionary multitask optimization Multifactorial evolutionary algorithm 

摘      要：Transfer Optimization is an incipient research area dedicated to solving multiple optimization tasks simultaneously. Among the different approaches that can address this problem effectively, Evolutionary Multitasking resorts to concepts from Evolutionary Computation to solve multiple problems within a single search process. In this paper we introduce a novel adaptive metaheuristic algorithm to deal with Evolutionary Multitasking environments coined as Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm (ATMFCGA). AT-MFCGA relies on cellular automata to implement mechanisms in order to exchange knowledge among the optimization problems under consideration. Furthermore, our approach is able to explain by itself the synergies among tasks that were encountered and exploited during the search, which helps us to understand interactions between related optimization tasks. A comprehensive experimental setup is designed to assess and compare the performance of AT-MFCGA to that of other renowned Evolutionary Multitasking alternatives (MFEA and MFEA-II). Experiments comprise 11 multitasking scenarios composed of 20 instances of 4 combinatorial optimization problems, yielding the largest discrete multitasking environment solved to date. Results are conclusive in regard to the superior quality of solutions provided by AT-MFCGA with respect to the rest of the methods, which are complemented by a quantitative examination of the genetic transferability among tasks throughout the search process. (c) 2021 Elsevier Inc. All rights reserved.