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.
Full text not available from this repository.Abstract
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.
| Item Type: | Article |
|---|---|
| Funders: | University of Malaya Research Grant (UMRG) RG 333-15AFR , Frontier Science Research Cluster |
| Uncontrolled Keywords: | Multi-objective particle swarm optimization; Swarm intelligence; Constrained multi-objective optimization; Fed-batch fermentation; Tumor treatment |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 14 May 2019 08:28 |
| Last Modified: | 14 May 2019 08:28 |
| URI: | http://eprints.um.edu.my/id/eprint/21228 |
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