IEICE TRANSACTIONS on Fundamentals
Particle Swarm Optimizers with Growing Tree Topology
Summary :
This paper presents a new particle swarm optimizer characterized by growing tree topology. If a particle is stagnated then a new particle is born and is located away from the trap. Depending on the property of objective problems, particles are born successively and the growing swarm constitutes a tree-topology. Performing numerical experiments for typical benchmarks, the algorithm efficiency is evaluated in several key measures such as success rate, the number of iterations and the number of particles. As compared with other basic PSOs, we can suggest that the proposed algorithm has efficient performance in optimization with low-cost computation.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E92-A No.9 pp.2275-2282
- Publication Date
- 2009/09/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E92.A.2275
- Type of Manuscript
- PAPER
- Category
- Nonlinear Problems
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Eiji MIYAGAWA, Toshimichi SAITO, "Particle Swarm Optimizers with Growing Tree Topology" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 9, pp. 2275-2282, September 2009, doi: 10.1587/transfun.E92.A.2275.
Abstract: This paper presents a new particle swarm optimizer characterized by growing tree topology. If a particle is stagnated then a new particle is born and is located away from the trap. Depending on the property of objective problems, particles are born successively and the growing swarm constitutes a tree-topology. Performing numerical experiments for typical benchmarks, the algorithm efficiency is evaluated in several key measures such as success rate, the number of iterations and the number of particles. As compared with other basic PSOs, we can suggest that the proposed algorithm has efficient performance in optimization with low-cost computation.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.2275/_p
Copy
@ARTICLE{e92-a_9_2275,
author={Eiji MIYAGAWA, Toshimichi SAITO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Particle Swarm Optimizers with Growing Tree Topology},
year={2009},
volume={E92-A},
number={9},
pages={2275-2282},
abstract={This paper presents a new particle swarm optimizer characterized by growing tree topology. If a particle is stagnated then a new particle is born and is located away from the trap. Depending on the property of objective problems, particles are born successively and the growing swarm constitutes a tree-topology. Performing numerical experiments for typical benchmarks, the algorithm efficiency is evaluated in several key measures such as success rate, the number of iterations and the number of particles. As compared with other basic PSOs, we can suggest that the proposed algorithm has efficient performance in optimization with low-cost computation.},
keywords={},
doi={10.1587/transfun.E92.A.2275},
ISSN={1745-1337},
month={September},}
Copy
TY - JOUR
TI - Particle Swarm Optimizers with Growing Tree Topology
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2275
EP - 2282
AU - Eiji MIYAGAWA
AU - Toshimichi SAITO
PY - 2009
DO - 10.1587/transfun.E92.A.2275
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2009
AB - This paper presents a new particle swarm optimizer characterized by growing tree topology. If a particle is stagnated then a new particle is born and is located away from the trap. Depending on the property of objective problems, particles are born successively and the growing swarm constitutes a tree-topology. Performing numerical experiments for typical benchmarks, the algorithm efficiency is evaluated in several key measures such as success rate, the number of iterations and the number of particles. As compared with other basic PSOs, we can suggest that the proposed algorithm has efficient performance in optimization with low-cost computation.
ER -
English
