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Crowding-based local differential evolution with speciation-based memory archive for dynamic multimodal optimization

Authors:

Subhodip Biswas,

Ponntuthurai N. SuganthanAuthors Info & Claims

GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation

Pages 33 - 40

https://doi.org/10.1145/2463372.2463392

Published: 06 July 2013 Publication History

Abstract

In the real world, many problems are multimodal as well as dynamic. Such problems require optimizers which not only locate multiple optima in a single run but also track the changing optima positions in the dynamic environments. In this paper a niching parameter free algorithm is designed which can locate multiple optima in changing environments. The proposed algorithm integrates the crowding concept with a competent Evolutionary Algorithm (EA) called Differential Evolution (DE) for maintaining the multiple peaks in a single run. To avoid the use of niching parameter that requires prior knowledge about the fitness landscape, the authors have used local mutation for searching the solution space. A speciation-based memory archive is integrated for regeneration of population after an environmental change is detected. Experimental analysis is conducted on the Moving Peaks Benchmark problem and the performance of the proposed algorithm is compared with other peer algorithms to highlight the overall effectiveness of our work.

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Abu Doush IAwadallah MAl-Betar MAlomari OMakhadmeh SAbasi AAlyasseri Z(2023)Archive-based coronavirus herd immunity algorithm for optimizing weights in neural networksNeural Computing and Applications10.1007/s00521-023-08577-y35:21(15923-15941)Online publication date: 19-Apr-2023
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Sheng WWang XWang ZLi QZheng YChen S(2022) A Differential Evolution Algorithm With Adaptive Niching and K -Means Operation for Data Clustering IEEE Transactions on Cybernetics10.1109/TCYB.2020.303588752:7(6181-6195)Online publication date: Jul-2022
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Index Terms

Crowding-based local differential evolution with speciation-based memory archive for dynamic multimodal optimization
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
    2. Numerical analysis
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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Published In

cover image ACM Conferences

GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation

July 2013

1672 pages

ISBN:9781450319638

DOI:10.1145/2463372

Editor:
Christian Blum
IKERBASQUE and University of the Basque Country UPV/EHU, Spain
,
General Chair:
Enrique Alba
University of Malaga, Spain

Copyright © 2013 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

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Published: 06 July 2013

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GECCO '13

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SIGEVO

GECCO '13: Genetic and Evolutionary Computation Conference

July 6 - 10, 2013

Amsterdam, The Netherlands

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GECCO '13 Paper Acceptance Rate 204 of 570 submissions, 36%;

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

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Cited By

Abu Doush ISultan KAlsaber AAlkandari DAbdullah A(2024)Enhanced Jaya optimization for improving multilayer perceptron neural network in urban air quality predictionJournal of Intelligent Systems10.1515/jisys-2023-031033:1Online publication date: 18-Jul-2024
https://doi.org/10.1515/jisys-2023-0310
Abu Doush IAwadallah MAl-Betar MAlomari OMakhadmeh SAbasi AAlyasseri Z(2023)Archive-based coronavirus herd immunity algorithm for optimizing weights in neural networksNeural Computing and Applications10.1007/s00521-023-08577-y35:21(15923-15941)Online publication date: 19-Apr-2023
https://doi.org/10.1007/s00521-023-08577-y
Sheng WWang XWang ZLi QZheng YChen S(2022) A Differential Evolution Algorithm With Adaptive Niching and K -Means Operation for Data Clustering IEEE Transactions on Cybernetics10.1109/TCYB.2020.303588752:7(6181-6195)Online publication date: Jul-2022
https://doi.org/10.1109/TCYB.2020.3035887
Huang TGong YChen WWang HZhang J(2022)A Probabilistic Niching Evolutionary Computation Framework Based on Binary Space PartitioningIEEE Transactions on Cybernetics10.1109/TCYB.2020.297290752:1(51-64)Online publication date: Jan-2022
https://doi.org/10.1109/TCYB.2020.2972907
Boulesnane AMeshoul S(2022)Do We Need Change Detection for Dynamic Optimization Problems?: A SurveyArtificial Intelligence and Its Applications10.1007/978-3-030-96311-8_13(132-142)Online publication date: 12-Mar-2022
https://doi.org/10.1007/978-3-030-96311-8_13
Ahrari AElsayed SSarker REssam DCoello Coello C(2021)Adaptive Multilevel Prediction Method for Dynamic Multimodal OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.305117225:3(463-477)Online publication date: Jun-2021
https://doi.org/10.1109/TEVC.2021.3051172
Singh STiwari AAgrawal S(2021)Differential Evolution Algorithm for Multimodal Optimization: A Short SurveySoft Computing for Problem Solving10.1007/978-981-16-2712-5_58(745-756)Online publication date: 14-Oct-2021
https://doi.org/10.1007/978-981-16-2712-5_58
Wang ZZhan ZLin YYu WWang HKwong SZhang J(2020)Automatic Niching Differential Evolution With Contour Prediction Approach for Multimodal Optimization ProblemsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2019.291072124:1(114-128)Online publication date: Feb-2020
https://doi.org/10.1109/TEVC.2019.2910721
Ben Ouada MBoudali ITagina MBelciug SGorunescu F(2019)A Hybrid Multiobjective Optimization Approach for Dynamic Problems: Evolutionary Algorithm Using Hypervolume IndicatorIntelligent Decision Support Systems—A Journey to Smarter Healthcare10.1007/978-3-030-14347-3_21(208-218)Online publication date: 21-Mar-2019
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Yalçınkaya AŞenoğlu BYolcu U(2018)Maximum likelihood estimation for the parameters of skew normal distribution using genetic algorithmSwarm and Evolutionary Computation10.1016/j.swevo.2017.07.00738(127-138)Online publication date: Feb-2018
https://doi.org/10.1016/j.swevo.2017.07.007
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