A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization
Due to increased search complexity in multi-objective optimization, premature convergence becomes a problem. Complex engineering problems poses high number of variables with many constraints. Hence, more difficult benchmark problems must be utilized to validate new algorithms performance. A well-kno...
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| Format: | Article |
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Elsevier
2018
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| _version_ | 1796961390797258752 |
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| author | Mohd Zain, Mohamad Zihin Kanesan, Jeevan Chuah, Joon Huang Dhanapal, Saroja Kendall, Graham |
| author_facet | Mohd Zain, Mohamad Zihin Kanesan, Jeevan Chuah, Joon Huang Dhanapal, Saroja Kendall, Graham |
| author_sort | Mohd Zain, Mohamad Zihin |
| collection | UM |
| description | Due to increased search complexity in multi-objective optimization, premature convergence becomes a problem. Complex engineering problems poses high number of variables with many constraints. Hence, more difficult benchmark problems must be utilized to validate new algorithms performance. A well-known optimizer, Multi-Objective Particle Swarm Optimizer (MOPSO), has a few weakness that needs to be addressed, specifically its convergence in high dimensional problems and its constraints handling capability. For these reasons, we propose a modified MOPSO (M-MOPSO) to improve upon these aspects. M-MOPSO is compared with four other algorithms namely, MOPSO, Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Evolutionary Algorithm based on Decompositions (MOEA/D) and Multi-Objective Differential Evolution (MODE). M-MOPSO emerged as the best algorithm in eight out of the ten constrained benchmark problems. It also shows promising results in bioprocess application problems and tumor treatment problems. In overall, M-MOPSO was able to solve multi-objective problems with good convergence and is suitable to be used in real world problem. |
| first_indexed | 2024-03-06T05:53:33Z |
| format | Article |
| id | um.eprints-21228 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:53:33Z |
| publishDate | 2018 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | um.eprints-212282019-05-14T08:28:17Z http://eprints.um.edu.my/21228/ A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization Mohd Zain, Mohamad Zihin Kanesan, Jeevan Chuah, Joon Huang Dhanapal, Saroja Kendall, Graham TK Electrical engineering. Electronics Nuclear engineering Due to increased search complexity in multi-objective optimization, premature convergence becomes a problem. Complex engineering problems poses high number of variables with many constraints. Hence, more difficult benchmark problems must be utilized to validate new algorithms performance. A well-known optimizer, Multi-Objective Particle Swarm Optimizer (MOPSO), has a few weakness that needs to be addressed, specifically its convergence in high dimensional problems and its constraints handling capability. For these reasons, we propose a modified MOPSO (M-MOPSO) to improve upon these aspects. M-MOPSO is compared with four other algorithms namely, MOPSO, Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Evolutionary Algorithm based on Decompositions (MOEA/D) and Multi-Objective Differential Evolution (MODE). M-MOPSO emerged as the best algorithm in eight out of the ten constrained benchmark problems. It also shows promising results in bioprocess application problems and tumor treatment problems. In overall, M-MOPSO was able to solve multi-objective problems with good convergence and is suitable to be used in real world problem. Elsevier 2018 Article PeerReviewed Mohd Zain, Mohamad Zihin and Kanesan, Jeevan and Chuah, Joon Huang and Dhanapal, Saroja and Kendall, Graham (2018) A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization. Applied Soft Computing, 70. pp. 680-700. ISSN 1568-4946, DOI https://doi.org/10.1016/j.asoc.2018.06.022 <https://doi.org/10.1016/j.asoc.2018.06.022>. https://doi.org/10.1016/j.asoc.2018.06.022 doi:10.1016/j.asoc.2018.06.022 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Mohd Zain, Mohamad Zihin Kanesan, Jeevan Chuah, Joon Huang Dhanapal, Saroja Kendall, Graham A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization |
| title | A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization |
| title_full | A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization |
| title_fullStr | A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization |
| title_full_unstemmed | A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization |
| title_short | A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization |
| title_sort | multi objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
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