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

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.

Automated summary

Transfer Optimization is a new research area focusing on solving multiple optimization tasks simultaneously, with Evolutionary Multitasking being one effective approach. In this paper, a novel Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm (AT-MFCGA) is introduced to handle Evolutionary Multitasking environments. AT-MFCGA relies on cellular automata for knowledge exchange and can independently explain synergies among tasks. Experimental results show the superior performance of AT-MFCGA compared to other methods in solving multiple optimization tasks.