An enhanced bacterial colony optimization with dynamic multi‐leader co‐evolution for multiobjective optimization problems
Abstract The information transfer mechanism within the population is an essential factor for population‐based multiobjective optimization algorithms. An efficient leader selection strategy can effectively help the population to approach the true Pareto front. However, traditional population‐based mu...
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crwiley:10.1111/exsy.13410 2024-06-02T08:05:49+00:00 An enhanced bacterial colony optimization with dynamic multi‐leader co‐evolution for multiobjective optimization problems Wang, Hong Wang, Yixin Liu, Menglong Zhou, Tianwei Niu, Ben National Natural Science Foundation of China Basic and Applied Basic Research Foundation of Guangdong Province 2023 http://dx.doi.org/10.1111/exsy.13410 https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.13410 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor Expert Systems volume 40, issue 10 ISSN 0266-4720 1468-0394 journal-article 2023 crwiley https://doi.org/10.1111/exsy.13410 2024-05-03T11:26:08Z Abstract The information transfer mechanism within the population is an essential factor for population‐based multiobjective optimization algorithms. An efficient leader selection strategy can effectively help the population to approach the true Pareto front. However, traditional population‐based multiobjective optimization algorithms are restricted to a single global leader and cannot transfer information efficiently. To overcome those limitations, in this paper, a multiobjective bacterial colony optimization with dynamic multi‐leader co‐evolution (MBCO/DML) is proposed, and a novel information transfer mechanism is developed within the group for adaptive evolution. Specifically, to enhance convergence and diversity, a multi‐leaders learning mechanism is designed based on a dynamically evolving elite archive via direction‐based hierarchical clustering. Finally, adaptive bacterial elimination is proposed to enable bacteria to escape from the local Pareto front according to convergence status. The results of numerical experiments show the superiority of the proposed algorithm in comparison with related population‐based multiobjective optimization algorithms on 24 frequently used benchmarks. This paper demonstrates the effectiveness of our dynamic leader selection in information transfer for improving both convergence and diversity to solve multiobjective optimization problems, which plays a significant role in information transfer of population evolution. Furthermore, we confirm the validity of the co‐evolution framework to the bacterial‐based optimization algorithm, greatly enhancing the searching capability for bacterial colony. Article in Journal/Newspaper DML Wiley Online Library Expert Systems 40 10 |
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Abstract The information transfer mechanism within the population is an essential factor for population‐based multiobjective optimization algorithms. An efficient leader selection strategy can effectively help the population to approach the true Pareto front. However, traditional population‐based multiobjective optimization algorithms are restricted to a single global leader and cannot transfer information efficiently. To overcome those limitations, in this paper, a multiobjective bacterial colony optimization with dynamic multi‐leader co‐evolution (MBCO/DML) is proposed, and a novel information transfer mechanism is developed within the group for adaptive evolution. Specifically, to enhance convergence and diversity, a multi‐leaders learning mechanism is designed based on a dynamically evolving elite archive via direction‐based hierarchical clustering. Finally, adaptive bacterial elimination is proposed to enable bacteria to escape from the local Pareto front according to convergence status. The results of numerical experiments show the superiority of the proposed algorithm in comparison with related population‐based multiobjective optimization algorithms on 24 frequently used benchmarks. This paper demonstrates the effectiveness of our dynamic leader selection in information transfer for improving both convergence and diversity to solve multiobjective optimization problems, which plays a significant role in information transfer of population evolution. Furthermore, we confirm the validity of the co‐evolution framework to the bacterial‐based optimization algorithm, greatly enhancing the searching capability for bacterial colony. |
author2 |
National Natural Science Foundation of China Basic and Applied Basic Research Foundation of Guangdong Province |
format |
Article in Journal/Newspaper |
author |
Wang, Hong Wang, Yixin Liu, Menglong Zhou, Tianwei Niu, Ben |
spellingShingle |
Wang, Hong Wang, Yixin Liu, Menglong Zhou, Tianwei Niu, Ben An enhanced bacterial colony optimization with dynamic multi‐leader co‐evolution for multiobjective optimization problems |
author_facet |
Wang, Hong Wang, Yixin Liu, Menglong Zhou, Tianwei Niu, Ben |
author_sort |
Wang, Hong |
title |
An enhanced bacterial colony optimization with dynamic multi‐leader co‐evolution for multiobjective optimization problems |
title_short |
An enhanced bacterial colony optimization with dynamic multi‐leader co‐evolution for multiobjective optimization problems |
title_full |
An enhanced bacterial colony optimization with dynamic multi‐leader co‐evolution for multiobjective optimization problems |
title_fullStr |
An enhanced bacterial colony optimization with dynamic multi‐leader co‐evolution for multiobjective optimization problems |
title_full_unstemmed |
An enhanced bacterial colony optimization with dynamic multi‐leader co‐evolution for multiobjective optimization problems |
title_sort |
enhanced bacterial colony optimization with dynamic multi‐leader co‐evolution for multiobjective optimization problems |
publisher |
Wiley |
publishDate |
2023 |
url |
http://dx.doi.org/10.1111/exsy.13410 https://onlinelibrary.wiley.com/doi/pdf/10.1111/exsy.13410 |
genre |
DML |
genre_facet |
DML |
op_source |
Expert Systems volume 40, issue 10 ISSN 0266-4720 1468-0394 |
op_rights |
http://onlinelibrary.wiley.com/termsAndConditions#vor |
op_doi |
https://doi.org/10.1111/exsy.13410 |
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Expert Systems |
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40 |
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10 |
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1800750699035754496 |