RT Journal Article
T1 AT-MFCGA: 												An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking
A1 Osaba, Eneko
A1 Del Ser, Javier
A1 Martinez, Aritz D.
A1 Lobo, Jesus L.
A1 Herrera, Francisco
AB 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 (AT-MFCGA). 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.
SN 0020-0255
YR 2021
FD 2021-09
LK https://hdl.handle.net/11556/3068
UL https://hdl.handle.net/11556/3068
LA eng
NO Osaba , E , Del Ser , J , Martinez , A D , Lobo , J L & Herrera , F 2021 , ' AT-MFCGA : An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking ' , Information Sciences , vol. 570 , pp. 577-598 . https://doi.org/10.1016/j.ins.2021.05.005
NO Publisher Copyright: © 2021 Elsevier Inc.
NO The authors thank the Basque Government for its support through the Consolidated Research Group MATHMODE (IT1294-19), as well as the ELKARTEK program (3KIA project, Ref. KK-2020/00049). Authors also acknowledge the financial support from the project SCOTT: Secure Connected Trustable Things (ECSEL Joint Undertaking, Ref. 737422). Francisco Herrera would like to thank the Spanish Government for its funding support (SMART-DaSCI project, TIN2017-89517-P). The authors thank the Basque Government for its support through the Consolidated Research Group MATHMODE (IT1294-19), as well as the ELKARTEK program (3KIA project, Ref. KK-2020/00049). Authors also acknowledge the financial support from the project SCOTT: Secure Connected Trustable Things (ECSEL Joint Undertaking, Ref. 737422). Francisco Herrera would like to thank the Spanish Government for its funding support (SMART-DaSCI project, TIN2017-89517-P).
DS TECNALIA Publications
RD 27 jul 2024