Particle swarm optimization with state-based adaptive velocity limit strategy

Velocity limit (VL) has been widely adopted in many variants of particle swarm optimization (PSO) to prevent particles from searching outside the solution space. Several adaptive VL strategies have been introduced with which the performance of PSO can be improved. However, the existing adaptive VL s...

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Main Authors: Li, Xinze, Mao, Kezhi, Lin, Fanfan, Zhang, Xin
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/160783
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1607832022-08-02T08:54:16Z Particle swarm optimization with state-based adaptive velocity limit strategy Li, Xinze Mao, Kezhi Lin, Fanfan Zhang, Xin School of Electrical and Electronic Engineering Interdisciplinary Graduate School (IGS) Engineering::Electrical and electronic engineering Adaptive Velocity Limit Evolutionary State Estimation Velocity limit (VL) has been widely adopted in many variants of particle swarm optimization (PSO) to prevent particles from searching outside the solution space. Several adaptive VL strategies have been introduced with which the performance of PSO can be improved. However, the existing adaptive VL strategies simply adjust their VL based on iterations, leading to unsatisfactory optimization results because of the incompatibility between VL and the current searching state of particles. To deal with this problem, a novel PSO variant with state-based adaptive velocity limit strategy (PSO-SAVL) is proposed. In the proposed PSO-SAVL, VL is adaptively adjusted based on the evolutionary state estimation (ESE) in which a high value of VL is set for global searching state and a low value of VL is set for local searching state. Besides that, limit handling strategies have been modified and adopted to improve the capability of avoiding local optima. The good performance of PSO-SAVL has been experimentally validated on a wide range of benchmark functions with 50 dimensions. The satisfactory scalability of PSO-SAVL in high-dimension and large-scale problems is also verified. Besides, the merits of the strategies in PSO-SAVL are verified in experiments. Sensitivity analysis for the relevant hyper-parameters in state-based adaptive VL strategy is conducted, and insights in how to select these hyper-parameters are also discussed. 2022-08-02T08:54:16Z 2022-08-02T08:54:16Z 2021 Journal Article Li, X., Mao, K., Lin, F. & Zhang, X. (2021). Particle swarm optimization with state-based adaptive velocity limit strategy. Neurocomputing, 447, 64-79. https://dx.doi.org/10.1016/j.neucom.2021.03.077 0925-2312 https://hdl.handle.net/10356/160783 10.1016/j.neucom.2021.03.077 2-s2.0-85103947656 447 64 79 en Neurocomputing © 2021 Elsevier B.V. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Adaptive Velocity Limit
Evolutionary State Estimation
spellingShingle Engineering::Electrical and electronic engineering
Adaptive Velocity Limit
Evolutionary State Estimation
Li, Xinze
Mao, Kezhi
Lin, Fanfan
Zhang, Xin
Particle swarm optimization with state-based adaptive velocity limit strategy
description Velocity limit (VL) has been widely adopted in many variants of particle swarm optimization (PSO) to prevent particles from searching outside the solution space. Several adaptive VL strategies have been introduced with which the performance of PSO can be improved. However, the existing adaptive VL strategies simply adjust their VL based on iterations, leading to unsatisfactory optimization results because of the incompatibility between VL and the current searching state of particles. To deal with this problem, a novel PSO variant with state-based adaptive velocity limit strategy (PSO-SAVL) is proposed. In the proposed PSO-SAVL, VL is adaptively adjusted based on the evolutionary state estimation (ESE) in which a high value of VL is set for global searching state and a low value of VL is set for local searching state. Besides that, limit handling strategies have been modified and adopted to improve the capability of avoiding local optima. The good performance of PSO-SAVL has been experimentally validated on a wide range of benchmark functions with 50 dimensions. The satisfactory scalability of PSO-SAVL in high-dimension and large-scale problems is also verified. Besides, the merits of the strategies in PSO-SAVL are verified in experiments. Sensitivity analysis for the relevant hyper-parameters in state-based adaptive VL strategy is conducted, and insights in how to select these hyper-parameters are also discussed.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Li, Xinze
Mao, Kezhi
Lin, Fanfan
Zhang, Xin
format Article
author Li, Xinze
Mao, Kezhi
Lin, Fanfan
Zhang, Xin
author_sort Li, Xinze
title Particle swarm optimization with state-based adaptive velocity limit strategy
title_short Particle swarm optimization with state-based adaptive velocity limit strategy
title_full Particle swarm optimization with state-based adaptive velocity limit strategy
title_fullStr Particle swarm optimization with state-based adaptive velocity limit strategy
title_full_unstemmed Particle swarm optimization with state-based adaptive velocity limit strategy
title_sort particle swarm optimization with state-based adaptive velocity limit strategy
publishDate 2022
url https://hdl.handle.net/10356/160783
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