research-article

A Repair Method for Differential Evolution with Combined Variants to Solve Dynamic Constrained Optimization Problems

Authors:

María-Yaneli Ameca-Alducin,

Efrén Mezura-Montes,

Nicandro Cruz-RamírezAuthors Info & Claims

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

Pages 241 - 248

https://doi.org/10.1145/2739480.2754786

Published: 11 July 2015 Publication History

Abstract

Repair methods, which usually require feasible solutions as reference, have been employed by Evolutionary Algorithms to solve constrained optimization problems. In this work, a novel repair method, which does not require feasible solutions as reference and inspired by the differential mutation, is added to an algorithm which uses two variants of differential evolution to solve dynamic constrained optimization problems. The proposed repair method replaces a local search operator with the aim to improve the overall performance of the algorithm in different frequencies of change in the constrained space. The proposed approach is compared against other recently proposed algorithms in an also recently proposed benchmark. The results show that the proposed improved algorithm outperforms its original version and provides a very competitive overall performance with different change frequencies.

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Cited By

Fang JLiu HGu F(2024)A constrained multi-objective evolutionary algorithm based on fitness landscape indicatorApplied Soft Computing10.1016/j.asoc.2024.112128(112128)Online publication date: Aug-2024
https://doi.org/10.1016/j.asoc.2024.112128
Gupta SSingh SSu RGao SBansal J(2023)Multiple Elite Individual Guided Piecewise Search-Based Differential EvolutionIEEE/CAA Journal of Automatica Sinica10.1109/JAS.2023.12301810:1(135-158)Online publication date: Jan-2023
https://doi.org/10.1109/JAS.2023.123018
Lu XTang KMenzel SYao X(2022)Dynamic Optimization in Fast-Changing Environments via Offline Evolutionary SearchIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.310434326:3(431-445)Online publication date: Jun-2022
https://doi.org/10.1109/TEVC.2021.3104343
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Index Terms

A Repair Method for Differential Evolution with Combined Variants to Solve Dynamic Constrained Optimization Problems
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Bio-inspired approaches
        Genetic algorithms
2. Mathematics of computing
  1. Continuous mathematics
    1. Continuous functions

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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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Consejo Nacional de Ciencia y Tecnología

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GECCO '15

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SIGEVO

GECCO '15: Genetic and Evolutionary Computation Conference

July 11 - 15, 2015

Madrid, Spain

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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

Fang JLiu HGu F(2024)A constrained multi-objective evolutionary algorithm based on fitness landscape indicatorApplied Soft Computing10.1016/j.asoc.2024.112128(112128)Online publication date: Aug-2024
https://doi.org/10.1016/j.asoc.2024.112128
Gupta SSingh SSu RGao SBansal J(2023)Multiple Elite Individual Guided Piecewise Search-Based Differential EvolutionIEEE/CAA Journal of Automatica Sinica10.1109/JAS.2023.12301810:1(135-158)Online publication date: Jan-2023
https://doi.org/10.1109/JAS.2023.123018
Lu XTang KMenzel SYao X(2022)Dynamic Optimization in Fast-Changing Environments via Offline Evolutionary SearchIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.310434326:3(431-445)Online publication date: Jun-2022
https://doi.org/10.1109/TEVC.2021.3104343
Hamza NElsayed SSarker REssam D(2022)Solving constrained problems with dynamic objective functions2022 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC55065.2022.9870354(1-8)Online publication date: 18-Jul-2022
https://doi.org/10.1109/CEC55065.2022.9870354
Cao ZWang ZFu YJia HTian F(2022)An adaptive differential evolution framework based on population feature informationInformation Sciences10.1016/j.ins.2022.07.043608(1416-1440)Online publication date: Aug-2022
https://doi.org/10.1016/j.ins.2022.07.043
Hamza NSarker REssam D(2020)Sensitivity-Based Change Detection for Dynamic Constrained OptimizationIEEE Access10.1109/ACCESS.2020.29991618(103900-103912)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2999161
Zou PRajora MLiang S(2018)A new algorithm based on evolutionary computation for hierarchically coupled constraint optimizationJournal of Scheduling10.1007/s10951-018-0572-221:5(545-563)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1007/s10951-018-0572-2
Ameca-Alducin MMezura-Montes ECruz-Ramírez N(2018)Dynamic differential evolution with combined variants and a repair method to solve dynamic constrained optimization problemsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-016-2353-122:2(541-570)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.1007/s00500-016-2353-1
Ameca-Alducin MHasani-Shoreh MNeumann F(2018)On the Use of Repair Methods in Differential Evolution for Dynamic Constrained OptimizationApplications of Evolutionary Computation10.1007/978-3-319-77538-8_55(832-847)Online publication date: 8-Mar-2018
https://doi.org/10.1007/978-3-319-77538-8_55
Zaheer HPant MMonakhov OMonakhova E(2016)A simple and efficient co-operative approach for solving multi-modal problems2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)10.1109/ICEEOT.2016.7755465(4001-4006)Online publication date: Mar-2016
https://doi.org/10.1109/ICEEOT.2016.7755465
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