Particle Swarm Optimizer Networks with Stochastic Connection for Improvement of Diversity Search Ability to Solve Multimodal Optimization Problems
Summary :
Particle swarm optimizer network (PSON) is one of the multi-swarm PSOs. In PSON, a population is divided into multiple sub-PSOs, each of which searches a solution space independently. Although PSON has a good solving performance, it may be trapped into a local optimum solution. In this paper, we introduce into PSON a dynamic stochastic network topology called “PSON with stochastic connection” (PSON-SC). In PSON-SC, each sub-PSO can be connected to the global best (gbest) information memory and refer to gbest stochastically. We show clearly herein that the diversity of PSON-SC is higher than that of PSON, while confirming the effectiveness of PSON-SC by many numerical simulations.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E100-A No.4 pp.996-1007
- Publication Date
- 2017/04/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E100.A.996
- Type of Manuscript
- PAPER
- Category
- Nonlinear Problems
Authors
Tomoyuki SASAKI
Tokyo City University
Hidehiro NAKANO
Tokyo City University
Arata MIYAUCHI
Tokyo City University
Akira TAGUCHI
Tokyo City University
Keyword
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Tomoyuki SASAKI, Hidehiro NAKANO, Arata MIYAUCHI, Akira TAGUCHI, "Particle Swarm Optimizer Networks with Stochastic Connection for Improvement of Diversity Search Ability to Solve Multimodal Optimization Problems" in IEICE TRANSACTIONS on Fundamentals,
vol. E100-A, no. 4, pp. 996-1007, April 2017, doi: 10.1587/transfun.E100.A.996.
Abstract: Particle swarm optimizer network (PSON) is one of the multi-swarm PSOs. In PSON, a population is divided into multiple sub-PSOs, each of which searches a solution space independently. Although PSON has a good solving performance, it may be trapped into a local optimum solution. In this paper, we introduce into PSON a dynamic stochastic network topology called “PSON with stochastic connection” (PSON-SC). In PSON-SC, each sub-PSO can be connected to the global best (gbest) information memory and refer to gbest stochastically. We show clearly herein that the diversity of PSON-SC is higher than that of PSON, while confirming the effectiveness of PSON-SC by many numerical simulations.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E100.A.996/_p
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@ARTICLE{e100-a_4_996,
author={Tomoyuki SASAKI, Hidehiro NAKANO, Arata MIYAUCHI, Akira TAGUCHI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Particle Swarm Optimizer Networks with Stochastic Connection for Improvement of Diversity Search Ability to Solve Multimodal Optimization Problems},
year={2017},
volume={E100-A},
number={4},
pages={996-1007},
abstract={Particle swarm optimizer network (PSON) is one of the multi-swarm PSOs. In PSON, a population is divided into multiple sub-PSOs, each of which searches a solution space independently. Although PSON has a good solving performance, it may be trapped into a local optimum solution. In this paper, we introduce into PSON a dynamic stochastic network topology called “PSON with stochastic connection” (PSON-SC). In PSON-SC, each sub-PSO can be connected to the global best (gbest) information memory and refer to gbest stochastically. We show clearly herein that the diversity of PSON-SC is higher than that of PSON, while confirming the effectiveness of PSON-SC by many numerical simulations.},
keywords={},
doi={10.1587/transfun.E100.A.996},
ISSN={1745-1337},
month={April},}
Copy
TY - JOUR
TI - Particle Swarm Optimizer Networks with Stochastic Connection for Improvement of Diversity Search Ability to Solve Multimodal Optimization Problems
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 996
EP - 1007
AU - Tomoyuki SASAKI
AU - Hidehiro NAKANO
AU - Arata MIYAUCHI
AU - Akira TAGUCHI
PY - 2017
DO - 10.1587/transfun.E100.A.996
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E100-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2017
AB - Particle swarm optimizer network (PSON) is one of the multi-swarm PSOs. In PSON, a population is divided into multiple sub-PSOs, each of which searches a solution space independently. Although PSON has a good solving performance, it may be trapped into a local optimum solution. In this paper, we introduce into PSON a dynamic stochastic network topology called “PSON with stochastic connection” (PSON-SC). In PSON-SC, each sub-PSO can be connected to the global best (gbest) information memory and refer to gbest stochastically. We show clearly herein that the diversity of PSON-SC is higher than that of PSON, while confirming the effectiveness of PSON-SC by many numerical simulations.
ER -
English
