A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization

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

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Official URL: https://doi.org/10.1016/j.asoc.2018.06.022

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
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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