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

Mohd Zain, Mohamad Zihin bin; Kanesan, Jeevan; Chuah, Joon Huang; Dhanapal, Saroja; Kendall, Graham

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.

Citation

Mohd Zain, M. Z. B., Kanesan, J., Chuah, J. H., Dhanapal, S., & Kendall, G. (2018). A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization. Applied Soft Computing, 70, 680-700. https://doi.org/10.1016/j.asoc.2018.06.022

• Journal Article Type: Article
• Acceptance Date: Jun 11, 2018
• Online Publication Date: Jun 20, 2018
• Publication Date: 2018-09
• Deposit Date: May 11, 2020
• Journal: Applied Soft Computing
• Print ISSN: 1568-4946
• Electronic ISSN: 1872-9681
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 70
• Pages: 680-700
• DOI: https://doi.org/10.1016/j.asoc.2018.06.022
• Public URL: https://nottingham-repository.worktribe.com/output/1854593
• Publisher URL: https://www.sciencedirect.com/science/article/abs/pii/S1568494618303557