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A survey on multi-objective evolutionary algorithms for many-objective problems

Authors:

Christian Lücken,

Benjamín Barán,

Carlos BrizuelaAuthors Info & Claims

Computational Optimization and Applications, Volume 58, Issue 3

Pages 707 - 756

https://doi.org/10.1007/s10589-014-9644-1

Published: 01 July 2014 Publication History

Abstract

Multi-objective evolutionary algorithms (MOEAs) are well-suited for solving several complex multi-objective problems with two or three objectives. However, as the number of conflicting objectives increases, the performance of most MOEAs is severely deteriorated. How to improve MOEAs' performance when solving many-objective problems, i.e. problems with four or more conflicting objectives, is an important issue since a large number of this type of problems exists in science and engineering; thus, several researchers have proposed different alternatives. This paper presents a review of the use of MOEAs in many-objective problems describing the evolution of the field, the methods that were developed, as well as the main findings and open questions that need to be answered in order to continue shaping the field.

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Multi-objective evolutionary algorithms (MOEAs) have shown their effectiveness in exploring a well converged and diversified approximation set for multi-objective optimization problems (MOPs) with 2 and 3 objectives. However, most of them perform poorly ...
Hyper multi-objective evolutionary algorithm for multi-objective optimization problems

Multi-objective optimization problems (MOPs) are very common in practice. To solve MOPs, many kinds of multi-objective evolutionary algorithms (MOEAs) are proposed. However, different MOEAs have different performances for different MOPs. Therefore, it ...

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Published In

cover image Computational Optimization and Applications

Computational Optimization and Applications Volume 58, Issue 3

July 2014

278 pages

ISSN:0926-6003

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Copyright © Copyright © 2014 Springer Science+Business Media New York.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 July 2014

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Ma LZhang ZDing YZhou ZBaeza-Yates RBonchi F(2024)Handling Varied Objectives by Online Decision MakingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671812(2130-2140)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671812
Guerrero CLera IJuiz C(2024)Distributed genetic algorithm for application placement in the compute continuum leveraging infrastructure nodes for optimizationFuture Generation Computer Systems10.1016/j.future.2024.05.044160:C(154-170)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.future.2024.05.044
Zhang WLiu JLiu YLiu JTan S(2024)A many-objective evolutionary algorithm under diversity-first selection based frameworkExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123949250:COnline publication date: 18-Jul-2024
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Zhang WLiu JLiu JLiu YTan S(2024)A dual distance dominance based evolutionary algorithm with selection-replacement operator for many-objective optimizationExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121244237:PAOnline publication date: 27-Feb-2024
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Paparella V(2022)Pursuing Optimal Trade-Off Solutions in Multi-Objective Recommender SystemsProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3547425(727-729)Online publication date: 12-Sep-2022
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Zheng YWang D(2022)A survey of recommender systems with multi-objective optimizationNeurocomputing10.1016/j.neucom.2021.11.041474:C(141-153)Online publication date: 14-Feb-2022
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Dai LZhang LChen ZDing W(2022)Collaborative granular sievingInformation Sciences: an International Journal10.1016/j.ins.2022.09.007613:C(288-308)Online publication date: 1-Oct-2022
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Petchrompo SCoit DBrintrup AWannakrairot AParlikad A(2022)A review of Pareto pruning methods for multi-objective optimizationComputers and Industrial Engineering10.1016/j.cie.2022.108022167:COnline publication date: 1-May-2022
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