A Multi-Variation Multifactorial Evolutionary Algorithm for Large-Scale Multi-Objective Optimization

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Detail(s)

Original languageEnglish
Pages (from-to)248-262
Number of pages15
Journal / PublicationIEEE Transactions on Evolutionary Computation
Volume26
Issue number2
Online published13 Oct 2021
Publication statusPublished - Apr 2022

Abstract

For solving large-scale multi-objective problems (LSMOPs), the transformation-based methods have shown promising search efficiency, which varies the original problem as a new simplified problem and performs the optimization in simplified spaces instead of the original problem space. Owing to the useful information provided by the simplified searching space, the performance of LSMOPs has been improved to some extent. However, it is worth noting that the original problem has changed after the variation, and there is thus no guarantee of the preservation of the original global or near-global optimum in the newly generated space. In this paper, we propose to solve LSMOPs via a multi-variation multifactorial evolutionary algorithm. In contrast to existing transformation-based methods, the proposed approach intends to conduct an evolutionary search on both the original space of the LSMOP and multiple simplified spaces constructed in a multi-variation manner concurrently. In this way, useful traits found along the search can be seamlessly transferred from the simplified problem spaces to the original problem space toward efficient problem-solving. Besides, since the evolutionary search is also performed in the original problem space, preserving the original global optimal solution can be guaranteed. To evaluate the performance of the proposed framework, comprehensive empirical studies are carried out on a set of LSMOPs with 2-3 objectives and 500-5000 variables. The experiment results highlight the efficiency and effectiveness of the proposed method compared to the state-of-the-art methods for large-scale multi-objective optimization.

Research Area(s)

  • Evolutionary Multitasking, Genetics, Large-scale Optimization, Multi-objective Optimization, Multifactorial Optimization, Multitasking, Optimization, Search problems, Sociology, Statistics, Task analysis