IEICE TRANSACTIONS on Information
Multi-Species Particle Swarm Optimizer for Multimodal Function Optimization
Summary :
This paper introduces a modified particle swarm optimizer (PSO) called the Multi-Species Particle Swarm Optimizer (MSPSO) for locating all the global minima of multi-modal functions. MSPSO extend the original PSO by dividing the particle swarm spatially into a multiple cluster called a species in a multi-dimensional search space. Each species explores a different area of the search space and tries to find out the global or local optima of that area. We test our MSPSO for several multi-modal functions with multiple global optima. Our MSPSO can successfully locate all the global optima of all the test functions, and in particular, can locate all 18 global optima of the two-dimensional Shubert function. We also examined how the performance of MSPSO depends on various algorithm parameters.
- Publication
- IEICE TRANSACTIONS on Information Vol.E89-D No.3 pp.1181-1187
- Publication Date
- 2006/03/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e89-d.3.1181
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence and Cognitive Science
Authors
Keyword
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Masao IWAMATSU, "Multi-Species Particle Swarm Optimizer for Multimodal Function Optimization" in IEICE TRANSACTIONS on Information,
vol. E89-D, no. 3, pp. 1181-1187, March 2006, doi: 10.1093/ietisy/e89-d.3.1181.
Abstract: This paper introduces a modified particle swarm optimizer (PSO) called the Multi-Species Particle Swarm Optimizer (MSPSO) for locating all the global minima of multi-modal functions. MSPSO extend the original PSO by dividing the particle swarm spatially into a multiple cluster called a species in a multi-dimensional search space. Each species explores a different area of the search space and tries to find out the global or local optima of that area. We test our MSPSO for several multi-modal functions with multiple global optima. Our MSPSO can successfully locate all the global optima of all the test functions, and in particular, can locate all 18 global optima of the two-dimensional Shubert function. We also examined how the performance of MSPSO depends on various algorithm parameters.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e89-d.3.1181/_p
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@ARTICLE{e89-d_3_1181,
author={Masao IWAMATSU, },
journal={IEICE TRANSACTIONS on Information},
title={Multi-Species Particle Swarm Optimizer for Multimodal Function Optimization},
year={2006},
volume={E89-D},
number={3},
pages={1181-1187},
abstract={This paper introduces a modified particle swarm optimizer (PSO) called the Multi-Species Particle Swarm Optimizer (MSPSO) for locating all the global minima of multi-modal functions. MSPSO extend the original PSO by dividing the particle swarm spatially into a multiple cluster called a species in a multi-dimensional search space. Each species explores a different area of the search space and tries to find out the global or local optima of that area. We test our MSPSO for several multi-modal functions with multiple global optima. Our MSPSO can successfully locate all the global optima of all the test functions, and in particular, can locate all 18 global optima of the two-dimensional Shubert function. We also examined how the performance of MSPSO depends on various algorithm parameters.},
keywords={},
doi={10.1093/ietisy/e89-d.3.1181},
ISSN={1745-1361},
month={March},}
Copy
TY - JOUR
TI - Multi-Species Particle Swarm Optimizer for Multimodal Function Optimization
T2 - IEICE TRANSACTIONS on Information
SP - 1181
EP - 1187
AU - Masao IWAMATSU
PY - 2006
DO - 10.1093/ietisy/e89-d.3.1181
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E89-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2006
AB - This paper introduces a modified particle swarm optimizer (PSO) called the Multi-Species Particle Swarm Optimizer (MSPSO) for locating all the global minima of multi-modal functions. MSPSO extend the original PSO by dividing the particle swarm spatially into a multiple cluster called a species in a multi-dimensional search space. Each species explores a different area of the search space and tries to find out the global or local optima of that area. We test our MSPSO for several multi-modal functions with multiple global optima. Our MSPSO can successfully locate all the global optima of all the test functions, and in particular, can locate all 18 global optima of the two-dimensional Shubert function. We also examined how the performance of MSPSO depends on various algorithm parameters.
ER -
English
