A multi-objective discrete particle swarm optimization method for particle routing in distributed particle filters
Distributed particle filters (PFs) have received extensive attention because of its excellent performance. Generally, it needs to transmit particles to multiple processing units (PUs) in order to improve the performance. However, how to balance communication costs and computation costs in particle r...
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| Vydáno v: | Knowledge-based systems Ročník 240; s. 108068 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Amsterdam
Elsevier B.V
15.03.2022
Elsevier Science Ltd |
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| ISSN: | 0950-7051, 1872-7409 |
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| Abstract | Distributed particle filters (PFs) have received extensive attention because of its excellent performance. Generally, it needs to transmit particles to multiple processing units (PUs) in order to improve the performance. However, how to balance communication costs and computation costs in particle routing is still an open problem. This paper presents a multi-objective discrete particle swarm optimization (PSO) algorithm to solve the problem. In the algorithm, the particle routing problem in distributed PFs is modeled as a multi-objective constrained optimization model for the first time. Following that, an improved hybrid discrete multi-objective PSO is proposed. Two new operators designed based on the problem’s characteristics, that is, a local search strategy based on molecular force and a constraint processing mechanism with greedy search, are developed to improve the performance of the proposed algorithm. By comparing with three commonly used methods on classical particle filters problems, experimental results show that the proposed algorithm is a highly competitive approach, and it can provide multiple high-quality Pareto optimal solutions for decision-makers to meet their different needs.
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| AbstractList | Distributed particle filters (PFs) have received extensive attention because of its excellent performance. Generally, it needs to transmit particles to multiple processing units (PUs) in order to improve the performance. However, how to balance communication costs and computation costs in particle routing is still an open problem. This paper presents a multi-objective discrete particle swarm optimization (PSO) algorithm to solve the problem. In the algorithm, the particle routing problem in distributed PFs is modeled as a multi-objective constrained optimization model for the first time. Following that, an improved hybrid discrete multi-objective PSO is proposed. Two new operators designed based on the problem's characteristics, that is, a local search strategy based on molecular force and a constraint processing mechanism with greedy search, are developed to improve the performance of the proposed algorithm. By comparing with three commonly used methods on classical particle filters problems, experimental results show that the proposed algorithm is a highly competitive approach, and it can provide multiple high-quality Pareto optimal solutions for decision-makers to meet their different needs. Distributed particle filters (PFs) have received extensive attention because of its excellent performance. Generally, it needs to transmit particles to multiple processing units (PUs) in order to improve the performance. However, how to balance communication costs and computation costs in particle routing is still an open problem. This paper presents a multi-objective discrete particle swarm optimization (PSO) algorithm to solve the problem. In the algorithm, the particle routing problem in distributed PFs is modeled as a multi-objective constrained optimization model for the first time. Following that, an improved hybrid discrete multi-objective PSO is proposed. Two new operators designed based on the problem’s characteristics, that is, a local search strategy based on molecular force and a constraint processing mechanism with greedy search, are developed to improve the performance of the proposed algorithm. By comparing with three commonly used methods on classical particle filters problems, experimental results show that the proposed algorithm is a highly competitive approach, and it can provide multiple high-quality Pareto optimal solutions for decision-makers to meet their different needs. [Display omitted] |
| ArticleNumber | 108068 |
| Author | Hou, Yun Gu, Feng Xu, Wenyang Hao, Guosheng Zhang, Yong |
| Author_xml | – sequence: 1 givenname: Yun orcidid: 0000-0002-9507-316X surname: Hou fullname: Hou, Yun organization: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China – sequence: 2 givenname: Guosheng surname: Hao fullname: Hao, Guosheng email: hgskd@jsnu.edu.cn organization: School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, 221116, China – sequence: 3 givenname: Yong surname: Zhang fullname: Zhang, Yong email: yongzh401@126.com organization: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China – sequence: 4 givenname: Feng orcidid: 0000-0001-5337-4282 surname: Gu fullname: Gu, Feng organization: Department of Computer Science, College of Staten Island, The City University of New York, Staten Island, NY 10314, USA – sequence: 5 givenname: Wenyang surname: Xu fullname: Xu, Wenyang organization: School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, 221116, China |
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| Keywords | 99-00 Local search 00-01 Constraint Particle filters Particle swarm optimization Particle routing |
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| SubjectTerms | Algorithms Computation Constraint Constraints Decision makers Decision making Function words Local search Multiple objective analysis Objectives Operators Optimization Optimization models Particle filters Particle routing Particle swarm optimization Performance enhancement Search methods |
| Title | A multi-objective discrete particle swarm optimization method for particle routing in distributed particle filters |
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