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Exploring the Frequency of Boundary Control Methods Activation in Metaheuristic Algorithms

Published:24 July 2023Publication History

ABSTRACT

Recently, Boundary Control Methods (BCMs) have become increasingly relevant in the field of metaheuristic algorithms. In this study, we investigate the relationship between the activation frequency of different BCMs and the problem's dimensionality. Additionally, we analyze each problem dimension independently. Our research primarily concentrates on the top three algorithms from the IEEE CEC 2020 competition: AGSK, IMODE, and j2020, utilizing the competition benchmark set to conduct experiments. Our findings provide valuable insights into the metaheuristic domain, underlining the significance of comprehending BCM activation patterns to improve algorithm design and benchmarking practices.

References

  1. Rick Boks, Anna V Kononova, and Hao Wang. 2021. Quantifying the impact of boundary constraint handling methods on differential evolution. In Proceedings of the Genetic and Evolutionary Computation Conference Companion. 1199--1207.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Janez Brest, Sao Greiner, Borko Boskovic, Marjan Mernik, and Viljem Zumer. 2006. Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE transactions on evolutionary computation 10, 6 (2006), 646--657.Google ScholarGoogle Scholar
  3. Janez Brest, Mirjam Sepesy Maučec, and Borko Bošković. 2019. The 100-digit challenge: Algorithm jde100. In 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 19--26.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Janez Brest, Mirjam Sepesy Maučec, and Borko Bošković. 2020. Differential evolution algorithm for single objective bound-constrained optimization: Algorithm j2020. In 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 1--8.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Fabio Caraffini, Anna V Kononova, and David Corne. 2019. Infeasibility and structural bias in differential evolution. Information Sciences 496 (2019), 161--179.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Nikolaus Hansen, Anne Auger, Raymond Ros, Olaf Mersmann, Tea Tušar, and Dimo Brockhoff. 2021. COCO: A platform for comparing continuous optimizers in a black-box setting. Optimization Methods and Software 36, 1 (2021), 114--144.Google ScholarGoogle ScholarCross RefCross Ref
  7. Sabine Helwig, Juergen Branke, and Sanaz Mostaghim. 2012. Experimental analysis of bound handling techniques in particle swarm optimization. IEEE Transactions on Evolutionary computation 17, 2 (2012), 259--271.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Tomas Kadavy, Adam Viktorin, Anezka Kazikova, Michal Pluhacek, and Roman Senkerik. 2022. Impact of boundary control methods on bound-constrained optimization benchmarking. IEEE Transactions on Evolutionary Computation 26, 6 (2022), 1271--1280.Google ScholarGoogle ScholarCross RefCross Ref
  9. Anna V Kononova, Fabio Caraffini, and Thomas Bäck. 2021. Differential evolution outside the box. Information Sciences 581 (2021), 587--604.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Ali Wagdy Mohamed, Anas A Hadi, Ali Khater Mohamed, and Noor H Awad. 2020. Evaluating the performance of adaptive GainingSharing knowledge based algorithm on CEC 2020 benchmark problems. In 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 1--8.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Karam M Sallam, Saber M Elsayed, Ripon K Chakrabortty, and Michael J Ryan. 2020. Improved multi-operator differential evolution algorithm for solving unconstrained problems. In 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 1--8.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. WK Wong and Chew Ing Ming. 2019. A review on metaheuristic algorithms: recent trends, benchmarking and applications. In 2019 7th International Conference on Smart Computing & Communications (ICSCC). IEEE, 1--5.Google ScholarGoogle ScholarCross RefCross Ref
  13. C. T. Yue, K. V. Price, P. N. Suganthan, J. J. Liang, M. Z. Ali, B. Y. Qu, N. H. Awad, and Partha P Biswas. 2019. Problem Definitions and Evaluation Criteria for the CEC 2020 Special Session and Competition on Single Objective Bound Constrained Numerical Optimization. Technical Report 201911 (2019).Google ScholarGoogle Scholar

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  • Published in

    cover image ACM Conferences
    GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation
    July 2023
    2519 pages
    ISBN:9798400701207
    DOI:10.1145/3583133

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    • Published: 24 July 2023

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