IEICE TRANSACTIONS on Information
Enhanced Particle Swarm Optimization with Self-Adaptation on Entropy-Based Inertia Weight
Summary :
The inertia weight is the control parameter that tunes the balance between the exploration and exploitation movements in particle swarm optimization searches. Since the introduction of inertia weight, various strategies have been proposed for determining the appropriate inertia weight value. This paper presents a brief review of the various types of inertia weight strategies which are classified and discussed in four categories: static, time varying, dynamic, and adaptive. Furthermore, a novel entropy-based gain regulator (EGR) is proposed to detect the evolutionary state of particle swarm optimization in terms of the distances from particles to the current global best. And then apply proper inertia weights with respect to the corresponding distinct states. Experimental results on five widely applied benchmark functions show that the EGR produced significant improvements of particle swarm optimization.
- Publication
- IEICE TRANSACTIONS on Information Vol.E99-D No.2 pp.324-331
- Publication Date
- 2016/02/01
- Publicized
- 2015/11/19
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2015EDP7304
- Type of Manuscript
- PAPER
- Category
- Fundamentals of Information Systems
Authors
Hei-Chia WANG
National Cheng Kung University
Che-Tsung YANG
National Cheng Kung University
Keyword
Latest Issue
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Hei-Chia WANG, Che-Tsung YANG, "Enhanced Particle Swarm Optimization with Self-Adaptation on Entropy-Based Inertia Weight" in IEICE TRANSACTIONS on Information,
vol. E99-D, no. 2, pp. 324-331, February 2016, doi: 10.1587/transinf.2015EDP7304.
Abstract: The inertia weight is the control parameter that tunes the balance between the exploration and exploitation movements in particle swarm optimization searches. Since the introduction of inertia weight, various strategies have been proposed for determining the appropriate inertia weight value. This paper presents a brief review of the various types of inertia weight strategies which are classified and discussed in four categories: static, time varying, dynamic, and adaptive. Furthermore, a novel entropy-based gain regulator (EGR) is proposed to detect the evolutionary state of particle swarm optimization in terms of the distances from particles to the current global best. And then apply proper inertia weights with respect to the corresponding distinct states. Experimental results on five widely applied benchmark functions show that the EGR produced significant improvements of particle swarm optimization.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2015EDP7304/_p
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@ARTICLE{e99-d_2_324,
author={Hei-Chia WANG, Che-Tsung YANG, },
journal={IEICE TRANSACTIONS on Information},
title={Enhanced Particle Swarm Optimization with Self-Adaptation on Entropy-Based Inertia Weight},
year={2016},
volume={E99-D},
number={2},
pages={324-331},
abstract={The inertia weight is the control parameter that tunes the balance between the exploration and exploitation movements in particle swarm optimization searches. Since the introduction of inertia weight, various strategies have been proposed for determining the appropriate inertia weight value. This paper presents a brief review of the various types of inertia weight strategies which are classified and discussed in four categories: static, time varying, dynamic, and adaptive. Furthermore, a novel entropy-based gain regulator (EGR) is proposed to detect the evolutionary state of particle swarm optimization in terms of the distances from particles to the current global best. And then apply proper inertia weights with respect to the corresponding distinct states. Experimental results on five widely applied benchmark functions show that the EGR produced significant improvements of particle swarm optimization.},
keywords={},
doi={10.1587/transinf.2015EDP7304},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - Enhanced Particle Swarm Optimization with Self-Adaptation on Entropy-Based Inertia Weight
T2 - IEICE TRANSACTIONS on Information
SP - 324
EP - 331
AU - Hei-Chia WANG
AU - Che-Tsung YANG
PY - 2016
DO - 10.1587/transinf.2015EDP7304
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E99-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2016
AB - The inertia weight is the control parameter that tunes the balance between the exploration and exploitation movements in particle swarm optimization searches. Since the introduction of inertia weight, various strategies have been proposed for determining the appropriate inertia weight value. This paper presents a brief review of the various types of inertia weight strategies which are classified and discussed in four categories: static, time varying, dynamic, and adaptive. Furthermore, a novel entropy-based gain regulator (EGR) is proposed to detect the evolutionary state of particle swarm optimization in terms of the distances from particles to the current global best. And then apply proper inertia weights with respect to the corresponding distinct states. Experimental results on five widely applied benchmark functions show that the EGR produced significant improvements of particle swarm optimization.
ER -
English
