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Many-Objective Evolutionary Algorithms: A Survey

Authors:

Xin YaoAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 48, Issue 1

Article No.: 13, Pages 1 - 35

https://doi.org/10.1145/2792984

Published: 29 September 2015 Publication History

Abstract

Multiobjective evolutionary algorithms (MOEAs) have been widely used in real-world applications. However, most MOEAs based on Pareto-dominance handle many-objective problems (MaOPs) poorly due to a high proportion of incomparable and thus mutually nondominated solutions. Recently, a number of many-objective evolutionary algorithms (MaOEAs) have been proposed to deal with this scalability issue. In this article, a survey of MaOEAs is reported. According to the key ideas used, MaOEAs are categorized into seven classes: relaxed dominance based, diversity-based, aggregation-based, indicator-based, reference set based, preference-based, and dimensionality reduction approaches. Several future research directions in this field are also discussed.

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Index Terms

Many-Objective Evolutionary Algorithms: A Survey
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Randomized search
2. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
      1. Continuous optimization
        Bio-inspired optimization
      2. Discrete optimization
        Optimization with randomized search heuristics
        Evolutionary algorithms

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ACM Computing Surveys Volume 48, Issue 1

September 2015

592 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/2808687

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering / University of Florida / Gainesville, FL 32611

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Published: 29 September 2015

Accepted: 01 June 2015

Revised: 01 April 2015

Received: 01 April 2014

Published in CSUR Volume 48, Issue 1

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Zheng WDoerr BLi XHandl J(2024)Hot off the Press: Runtime Analysis for the NSGA-II: Proving, Quantifying, and Explaining the Inefficiency For Many ObjectivesProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664061(67-68)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3664061
Ghodsi STahmasebi SJalili MMoradi PLi XHandl J(2024)Many-Objective Evolutionary Optimization using Density Peaks Scoring Selection StrategyProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3654217(331-334)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3654217
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