A user-friendly Bees Algorithm for continuous and combinatorial optimisation
AbstractThis paper introduces a new variant of the Bees Algorithm (BA) called Bees Algorithm with 2-parameter (BA2), which is a population-based metaheuristic algorithm designed to solve continuous and combinatorial optimisation problems. The proposed algorithm simplified the BA’s parameters by comb...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/23311916.2023.2278257 |
| _version_ | 1797258463805440000 |
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| author | Asrul Harun Ismail Wegie Ruslan Duc Truong Pham |
| author_facet | Asrul Harun Ismail Wegie Ruslan Duc Truong Pham |
| author_sort | Asrul Harun Ismail |
| collection | DOAJ |
| description | AbstractThis paper introduces a new variant of the Bees Algorithm (BA) called Bees Algorithm with 2-parameter (BA2), which is a population-based metaheuristic algorithm designed to solve continuous and combinatorial optimisation problems. The proposed algorithm simplified the BA’s parameters by combining exploration and exploitation strategies while preserving the algorithm’s core principles to efficiently search for optimal solutions. The paper provides a detailed description of the algorithm’s core principles and its application to two engineering problems, the air-cooling system design (ACSD) and the printed circuit board assembly sequence optimisation (PASO). The results show that BA2 outperforms previous versions of the basic BA in terms of convergence speed and solution quality. However, the authors acknowledge that further research is needed to test the scalability and generalisability of the algorithm to larger and more diverse optimisation problems. Overall, this paper provides valuable insights into the potential of metaheuristics for solving real-world optimisation problems. |
| first_indexed | 2024-03-08T23:53:57Z |
| format | Article |
| id | doaj.art-9bd526d170ef443f89f323af1e97f696 |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-04-24T22:53:56Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-9bd526d170ef443f89f323af1e97f6962024-03-18T10:22:11ZengTaylor & Francis GroupCogent Engineering2331-19162023-12-0110210.1080/23311916.2023.2278257A user-friendly Bees Algorithm for continuous and combinatorial optimisationAsrul Harun Ismail0Wegie Ruslan1Duc Truong Pham2Centre of Operational Research and Logistics, School of Mathematics and Physics, University of Portsmouth, Portsmouth, UKDepartment of Industrial Engineering, University of Pancasila, Jakarta, IndonesiaBees Algorithm Research Group, School of Engineering, University of Birmingham, Birmingham, UKAbstractThis paper introduces a new variant of the Bees Algorithm (BA) called Bees Algorithm with 2-parameter (BA2), which is a population-based metaheuristic algorithm designed to solve continuous and combinatorial optimisation problems. The proposed algorithm simplified the BA’s parameters by combining exploration and exploitation strategies while preserving the algorithm’s core principles to efficiently search for optimal solutions. The paper provides a detailed description of the algorithm’s core principles and its application to two engineering problems, the air-cooling system design (ACSD) and the printed circuit board assembly sequence optimisation (PASO). The results show that BA2 outperforms previous versions of the basic BA in terms of convergence speed and solution quality. However, the authors acknowledge that further research is needed to test the scalability and generalisability of the algorithm to larger and more diverse optimisation problems. Overall, this paper provides valuable insights into the potential of metaheuristics for solving real-world optimisation problems.https://www.tandfonline.com/doi/10.1080/23311916.2023.2278257bees Algorithmnature-inspired algorithmbee-inspired algorithmmetaheuristicscontinuous optimisation problemcombinatorial optimisation problem |
| spellingShingle | Asrul Harun Ismail Wegie Ruslan Duc Truong Pham A user-friendly Bees Algorithm for continuous and combinatorial optimisation Cogent Engineering bees Algorithm nature-inspired algorithm bee-inspired algorithm metaheuristics continuous optimisation problem combinatorial optimisation problem |
| title | A user-friendly Bees Algorithm for continuous and combinatorial optimisation |
| title_full | A user-friendly Bees Algorithm for continuous and combinatorial optimisation |
| title_fullStr | A user-friendly Bees Algorithm for continuous and combinatorial optimisation |
| title_full_unstemmed | A user-friendly Bees Algorithm for continuous and combinatorial optimisation |
| title_short | A user-friendly Bees Algorithm for continuous and combinatorial optimisation |
| title_sort | user friendly bees algorithm for continuous and combinatorial optimisation |
| topic | bees Algorithm nature-inspired algorithm bee-inspired algorithm metaheuristics continuous optimisation problem combinatorial optimisation problem |
| url | https://www.tandfonline.com/doi/10.1080/23311916.2023.2278257 |
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