Cluster-Based Multiobjective Particle Swarm Optimization and Application for Chemical Plants
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Author(s)
Hong, Seokyoung
Lee, Jaewon
Cho, Hyungtae
Jang, Kyojin
Kim, Junghwan
Date Issued
February 28, 2023
Publisher
Hindawi
Citation
Seokyoung Hong, Jaewon Lee, Hyungtae Cho, Kyojin Jang, and Junghwan Kim, “Cluster-Based Multiobjective Particle Swarm Optimization and Application for Chemical Plants,” International Journal of Intelligent Systems, vol. 2023, Article ID 5275262, 13 pages, 2023. doi:10.1155/2023/5275262
Abstract
In multiobjective particle swarm optimization (MOPSO), the global-best particle is randomly selected for each population particle from a nondominated solution set. However, this Roulette wheel-based global particle selection is ineffective for convergence and diversity when the problem has numerous decision variables or a large number of global-best candidates. Thus, this study proposes the cluster-based MOPSO (CMOPSO). In CMOPSO, the similarities between particles are considered when selecting the global-best particle. The cluster for each particle is determined based on the Euclidean distance in the decision or objective space. The proposed approach is demonstrated by applying an operating condition optimization problem to the hydrogen production process. The target process is a representative chemical plant with a large search space and strong nonlinearity. Furthermore, the performance of CMOPSO is assessed by comparing it with that of MOPSO. The results indicate that CMOPSO considered in the decision space exhibits superior performance in terms of convergence and diversity.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Attribution 4.0 International
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1155/2023/5275262
