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Belief Rule-Based Adaptive Particle Swarm Optimization
Department of Computer Science and Engineering, University of Chittagong, Bangladesh.
University of Chittagong, Bangladesh.ORCID iD: 0000-0002-7473-8185
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0002-3090-7645
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-0244-3561
2021 (English)In: Data Science and Big Data Analytics in Smart Environments / [ed] Marta Chinnici; Florin Pop; Cătălin Negru, Taylor & Francis, 2021, 1, p. 88-107Chapter in book (Refereed)
Abstract [en]

Particle Swarm Optimization (PSO) is a meta-heuristic algorithm which is successfully applied to enormous applications to solve non-linear and complex high-dimensional optimization problems. The performance of PSO is greatly influenced by the tuning parameters. Due to the presence of uncertainty or noise in the optimization problem of different domains and to maintain a balance between exploration and exploitation in the search space, these parameters need to be adjusted. Therefore, it is crucial to identify the optimal values of the tuning parameters. In this paper, a new Belief Rule-Based Adaptive Particle Swarm Optimization (BRBAPSO) is proposed where the tuning parameters are adjusted dynamically by considering uncertainties, which ensure a balance between exploitation and exploration in the search space. Two Variants of BRBAPSO, namely Conjunctive BRBAPSO and Disjunctive BRBAPSO, are introduced and they are compared with Time-Varying Inertia Weight PSO (TVIW-PSO), Time-Varying Acceleration Coefficient PSO (TVAC-PSO), and Fuzzy Adaptive PSO (FAPSO) using the CEC 2013 real-parameter optimization benchmark functions. The results show that both variants of BRBAPSO outperform other algorithms on the benchmark functions.

Place, publisher, year, edition, pages
Taylor & Francis, 2021, 1. p. 88-107
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-86443DOI: 10.1201/9780367814397-5OAI: oai:DiVA.org:ltu-86443DiVA, id: diva2:1581693
Note

ISBN för värdpublikation: 978-0-367-40713-1; 978-0-367-77603-9;  978-0-367-81439-7

Available from: 2021-07-23 Created: 2021-07-23 Last updated: 2023-09-05Bibliographically approved

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Hossain, Mohammad ShahadatIslam, Raihan UlAndersson, Karl

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