survey

Indicator-based Multi-objective Evolutionary Algorithms: A Comprehensive Survey

Authors:

Jesús Guillermo Falcón-Cardona,

Carlos A. Coello CoelloAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 53, Issue 2

Article No.: 29, Pages 1 - 35

https://doi.org/10.1145/3376916

Published: 20 March 2020 Publication History

Abstract

For over 25 years, most multi-objective evolutionary algorithms (MOEAs) have adopted selection criteria based on Pareto dominance. However, the performance of Pareto-based MOEAs quickly degrades when solving multi-objective optimization problems (MOPs) having four or more objective functions (the so-called many-objective optimization problems), mainly because of the loss of selection pressure. Consequently, in recent years, MOEAs have been coupled with indicator-based selection mechanisms in furtherance of increasing the selection pressure so that they can properly solve many-objective optimization problems. Several research efforts have been conducted since 2003 regarding the design of the so-called indicator-based (IB) MOEAs. In this article, we present a comprehensive survey of IB-MOEAs for continuous search spaces since their origins up to the current state-of-the-art approaches. We propose a taxonomy that classifies IB-mechanisms into two main categories: (1) IB-Selection (which is divided into IB-Environmental Selection, IB-Density Estimation, and IB-Archiving) and (2) IB-Mating Selection. Each of these classes is discussed in detail in this article, emphasizing the advantages and drawbacks of the selection mechanisms. In the final part, we provide some possible paths for future research.

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Index Terms

Indicator-based Multi-objective Evolutionary Algorithms: A Comprehensive Survey
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Continuous space search
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization
      1. Continuous optimization
        Bio-inspired optimization

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 53, Issue 2

March 2021

848 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3388460

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 20 March 2020

Accepted: 01 December 2019

Revised: 01 May 2019

Received: 01 February 2019

Published in CSUR Volume 53, Issue 2

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https://doi.org/10.1155/2024/2311998
Zhu XRuan GGeng HLiu HBai MPeng C(2024)Multi-Objective Sizing Optimization Method of Microgrid Considering Cost and Carbon EmissionsIEEE Transactions on Industry Applications10.1109/TIA.2024.339557060:4(5565-5576)Online publication date: Jul-2024
https://doi.org/10.1109/TIA.2024.3395570
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Lu CLiu YZhang Q(2024)MOEA/D-CMA Made Better with (l+l)-CMA-ES2024 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC60901.2024.10612007(1-8)Online publication date: 30-Jun-2024
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