A Comprehensive Review on Evolutionary Algorithm Solving Multi-Objective Problems

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Abstract

In the real world, it is challenging to determine optimal solutions over multiple conflicting objectives in complex systems. As a mainstream method for solving multi-objective problems, the development and the application of Evolutionary Algorithm (EA) methods have attracted thousands of researches since the 1950s. However, as we know, there are few studies on the comprehensive review of multi-objective EA (MOEA) methods in general domains. In this review research, firstly, the categories of MOEA methods according to the classification strategy of reproduction operators is proposed. Then, a systematic literature search methodology and logical citation management are introduced in order to create a literature pool for further analysis. On the basis of the literature pool, the categories of MOEA methods concerning three aspects and the application domains are analyzed. The purpose of this review is to provide a comprehensive view and a guide reference for the MOEA method selection on solving a specific type of multi-objective optimization problems (MOPs).
Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Industrial Technology (ICIT) 2021
Number of pages8
Publication dateMay 2021
Publication statusPublished - May 2021

Keywords

  • Multi-objective optimization
  • Evolutionary Algorithm
  • Literature review
  • MOEA category

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