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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Veröffentlicht in:Knowledge-based systems Jg. 240; S. 108068
Hauptverfasser: Hou, Yun, Hao, Guosheng, Zhang, Yong, Gu, Feng, Xu, Wenyang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 15.03.2022
Elsevier Science Ltd
Schlagworte:
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
99-00
Local search
00-01
Constraint
Particle filters
Particle swarm optimization
Particle routing
ISSN:0950-7051, 1872-7409
Online-Zugang:Volltext
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Zusammenfassung: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]
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2021.108068