research-article

On Maintaining Diversity in MOEA/D: Application to a Biobjective Combinatorial FJSP

Authors:

Juan José Palacios Alonso,

Bilel DerbelAuthors Info & Claims

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

Pages 719 - 726

https://doi.org/10.1145/2739480.2754774

Published: 11 July 2015 Publication History

Abstract

MOEA/D is a generic decomposition-based multiobjective optimization framework which has been proved to be extremely effective in solving a broad range of optimization problems especially for continuous domains. In this paper, we consider applying MOEA/D to solve a bi-objective scheduling combinatorial problem in which task durations and due-dates are uncertain. Surprisingly, we find that the conventional MOEA/D implementation provides poor performance in our application setting. We show that this is because the replacement strategy underlying MOEA/D is suffering some shortcomes that lead to low population diversity, and thus to premature convergence. Consequently, we investigate existing variants of MOEA/D and we propose a novel and simple alternative replacement component at the aim of maintaining population diversity. Through extensive experiments, we then provide a comprehensive analysis on the relative performance and the behavior of the considered algorithms. Besides being able to outperform existing MOEA/D variants, as well as the standard NSGA-II algorithm, our investigations provide new insights into the search ability of MOEA/D and highlight new research opportunities for improving its design components.

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Cited By

Wang JZheng YHuang PPeng HWu Z(2024)A stable-state multi-objective evolutionary algorithm based on decompositionExpert Systems with Applications10.1016/j.eswa.2023.122452239(122452)Online publication date: Apr-2024
https://doi.org/10.1016/j.eswa.2023.122452
Jiang BLei HLi WWang R(2022)A novel multi-objective evolutionary algorithm for hybrid renewable energy system designSwarm and Evolutionary Computation10.1016/j.swevo.2022.101186(101186)Online publication date: Oct-2022
https://doi.org/10.1016/j.swevo.2022.101186
Strnad DKohek Š(2021)Constrained Multi-Objective Optimization of Simulated Tree Pruning with Heterogeneous CriteriaApplied Sciences10.3390/app11221078111:22(10781)Online publication date: 15-Nov-2021
https://doi.org/10.3390/app112210781
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Index Terms

On Maintaining Diversity in MOEA/D: Application to a Biobjective Combinatorial FJSP
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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cover image ACM Conferences

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

July 2015

1496 pages

ISBN:9781450334723

DOI:10.1145/2739480

Editor:
Sara Silva
Universidade de Lisboa, Portugal
,
General Chair:
Anna I. Esparcia-Alcázar
Universitat Politècnica de València, Spain

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 July 2015

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Spanish Government
Principality of Asturias

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GECCO '15

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SIGEVO

GECCO '15: Genetic and Evolutionary Computation Conference

July 11 - 15, 2015

Madrid, Spain

Acceptance Rates

GECCO '15 Paper Acceptance Rate 182 of 505 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Wang JZheng YHuang PPeng HWu Z(2024)A stable-state multi-objective evolutionary algorithm based on decompositionExpert Systems with Applications10.1016/j.eswa.2023.122452239(122452)Online publication date: Apr-2024
https://doi.org/10.1016/j.eswa.2023.122452
Jiang BLei HLi WWang R(2022)A novel multi-objective evolutionary algorithm for hybrid renewable energy system designSwarm and Evolutionary Computation10.1016/j.swevo.2022.101186(101186)Online publication date: Oct-2022
https://doi.org/10.1016/j.swevo.2022.101186
Strnad DKohek Š(2021)Constrained Multi-Objective Optimization of Simulated Tree Pruning with Heterogeneous CriteriaApplied Sciences10.3390/app11221078111:22(10781)Online publication date: 15-Nov-2021
https://doi.org/10.3390/app112210781
González-Rodríguez IPuente JPalacios JVela C(2020)Multi-objective evolutionary algorithm for solving energy-aware fuzzy job shop problemsSoft Computing10.1007/s00500-020-04940-6Online publication date: 6-May-2020
https://doi.org/10.1007/s00500-020-04940-6
Palacios JVela CGonzález-Rodríguez IPuente J(2017)A Memetic Algorithm for Due-Date Satisfaction in Fuzzy Job Shop SchedulingNatural and Artificial Computation for Biomedicine and Neuroscience10.1007/978-3-319-59740-9_14(135-145)Online publication date: 27-May-2017
https://doi.org/10.1007/978-3-319-59740-9_14
Derbel BLiefooghe AZhang QAguirre HTanaka K(2016)Multi-objective Local Search Based on DecompositionParallel Problem Solving from Nature – PPSN XIV10.1007/978-3-319-45823-6_40(431-441)Online publication date: 31-Aug-2016
https://doi.org/10.1007/978-3-319-45823-6_40

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