Deterministic Particle Swarm Optimizer with the Convergence and Divergence Dynamics
Summary :
In this paper, we propose a new paradigm of deterministic PSO, named piecewise-linear particle swarm optimizer (PPSO). In PPSO, each particle has two search dynamics, a convergence mode and a divergence mode. The trajectory of each particle is switched between the two dynamics and is controlled by parameters. We analyze convergence condition of each particle and investigate parameter conditions to allow particles to converge to an equilibrium point through numerical experiments. We further compare solving performances of PPSO. As a result, we report here that the solving performances of PPSO are substantially the same as or superior to those of PSO.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E100-A No.5 pp.1244-1247
- Publication Date
- 2017/05/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E100.A.1244
- Type of Manuscript
- LETTER
- Category
- Nonlinear Problems
Authors
Tomoyuki SASAKI
Japan Society for the Promotion of Science,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, "Deterministic Particle Swarm Optimizer with the Convergence and Divergence Dynamics" in IEICE TRANSACTIONS on Fundamentals,
vol. E100-A, no. 5, pp. 1244-1247, May 2017, doi: 10.1587/transfun.E100.A.1244.
Abstract: In this paper, we propose a new paradigm of deterministic PSO, named piecewise-linear particle swarm optimizer (PPSO). In PPSO, each particle has two search dynamics, a convergence mode and a divergence mode. The trajectory of each particle is switched between the two dynamics and is controlled by parameters. We analyze convergence condition of each particle and investigate parameter conditions to allow particles to converge to an equilibrium point through numerical experiments. We further compare solving performances of PPSO. As a result, we report here that the solving performances of PPSO are substantially the same as or superior to those of PSO.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E100.A.1244/_p
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@ARTICLE{e100-a_5_1244,
author={Tomoyuki SASAKI, Hidehiro NAKANO, Arata MIYAUCHI, Akira TAGUCHI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Deterministic Particle Swarm Optimizer with the Convergence and Divergence Dynamics},
year={2017},
volume={E100-A},
number={5},
pages={1244-1247},
abstract={In this paper, we propose a new paradigm of deterministic PSO, named piecewise-linear particle swarm optimizer (PPSO). In PPSO, each particle has two search dynamics, a convergence mode and a divergence mode. The trajectory of each particle is switched between the two dynamics and is controlled by parameters. We analyze convergence condition of each particle and investigate parameter conditions to allow particles to converge to an equilibrium point through numerical experiments. We further compare solving performances of PPSO. As a result, we report here that the solving performances of PPSO are substantially the same as or superior to those of PSO.},
keywords={},
doi={10.1587/transfun.E100.A.1244},
ISSN={1745-1337},
month={May},}
Copy
TY - JOUR
TI - Deterministic Particle Swarm Optimizer with the Convergence and Divergence Dynamics
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1244
EP - 1247
AU - Tomoyuki SASAKI
AU - Hidehiro NAKANO
AU - Arata MIYAUCHI
AU - Akira TAGUCHI
PY - 2017
DO - 10.1587/transfun.E100.A.1244
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E100-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2017
AB - In this paper, we propose a new paradigm of deterministic PSO, named piecewise-linear particle swarm optimizer (PPSO). In PPSO, each particle has two search dynamics, a convergence mode and a divergence mode. The trajectory of each particle is switched between the two dynamics and is controlled by parameters. We analyze convergence condition of each particle and investigate parameter conditions to allow particles to converge to an equilibrium point through numerical experiments. We further compare solving performances of PPSO. As a result, we report here that the solving performances of PPSO are substantially the same as or superior to those of PSO.
ER -
English
