An improved fault-tolerant cultural-PSO with probability for multi-AGV path planning

Lin, S; Liu, A; Wang, J; Kong, X

An improved fault-tolerant cultural-PSO with probability for multi-AGV path planning

Lin, S Liu, A Wang, J

Kong, X

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Expert Systems with Applications, 2024, 237, pp. 121510
Issue Date:: 2024-03-01

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Field	Value	Language
dc.contributor.author	Lin, S
dc.contributor.author	Liu, A
dc.contributor.author	Wang, J https://orcid.org/0000-0002-6282-0456
dc.contributor.author	Kong, X
dc.date.accessioned	2024-01-19T04:46:20Z
dc.date.available	2024-01-19T04:46:20Z
dc.date.issued	2024-03-01
dc.identifier.citation	Expert Systems with Applications, 2024, 237, pp. 121510
dc.identifier.issn	0957-4174
dc.identifier.uri	http://hdl.handle.net/10453/174804
dc.description.abstract	This paper presents a hybrid evolutionary algorithm, cultural-particle swarm optimization (C-PSO), which is inspired by the cultural algorithm and the particle swarm optimization algorithm. It is aimed to balance the performance of exploration and exploitation and avoid trapping in the local optima. It introduces a probabilistic approach to update the inertia weight based on the improved metropolis rule. Generating the optimal path without collisions is challenging to ensure vehicles operate safely in real-time implementation. The contributions of C-PSO are to solve the path planning problem of multiple vehicles in modern industrial warehouses, achieving task allocation, fault tolerance and collision avoidance by a dual-layer framework. It was compared with the other algorithms, including PSO, PSO-GA, CA, HS, ABC, HPSGWO, TS and MA, by CEC benchmark functions and statistical tests to demonstrate its great performance with fewer iterations and runtime and the best solutions. It is validated through computational experiments, which involve 15 AGVs and 20 tasks for demonstration.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Expert Systems with Applications
dc.relation.isbasedon	10.1016/j.eswa.2023.121510
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	An improved fault-tolerant cultural-PSO with probability for multi-AGV path planning
dc.type	Journal Article
utslib.citation.volume	237
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2026-03-01T00:00:00+1000Z
dc.date.updated	2024-01-19T04:46:19Z
pubs.publication-status	Accepted
pubs.volume	237

Abstract:

This paper presents a hybrid evolutionary algorithm, cultural-particle swarm optimization (C-PSO), which is inspired by the cultural algorithm and the particle swarm optimization algorithm. It is aimed to balance the performance of exploration and exploitation and avoid trapping in the local optima. It introduces a probabilistic approach to update the inertia weight based on the improved metropolis rule. Generating the optimal path without collisions is challenging to ensure vehicles operate safely in real-time implementation. The contributions of C-PSO are to solve the path planning problem of multiple vehicles in modern industrial warehouses, achieving task allocation, fault tolerance and collision avoidance by a dual-layer framework. It was compared with the other algorithms, including PSO, PSO-GA, CA, HS, ABC, HPSGWO, TS and MA, by CEC benchmark functions and statistical tests to demonstrate its great performance with fewer iterations and runtime and the best solutions. It is validated through computational experiments, which involve 15 AGVs and 20 tasks for demonstration.

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http://hdl.handle.net/10453/174804