Cask theory based parameter optimization for particle swarm optimization

Zenghui Wang; Yanxia Sun

doi:10.1007/978-3-642-38703-6_16

Cask theory based parameter optimization for particle swarm optimization

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

To avoid the bored try and error method of finding a set of parameters of Particle Swarm Optimization (PSO) and achieve good optimization performance, it is desired to get an adaptive optimization method to search a good set of parameters. A nested optimization method is proposed in this paper and it can be used to search the tuned parameters such as inertia weight ω, acceleration coefficients c₁ and c₂, and so on. This method considers the cask theory to achieve a better optimization performance. Several famous benchmarks were used to validate the proposed method and the simulation results showed the efficiency of the proposed method.

Original language	English
Title of host publication	Advances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings
Pages	137-143
Number of pages	7
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-38703-6_16
Publication status	Published - 2013
Externally published	Yes
Event	4th International Conference on Advances in Swarm Intelligence, ICSI 2013 - Harbin, China Duration: 12 Jun 2012 → 15 Jun 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	7928 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th International Conference on Advances in Swarm Intelligence, ICSI 2013
Country/Territory	China
City	Harbin
Period	12/06/12 → 15/06/12

Keywords

Cask theory
Nested Optimization method
PSO
Parameter Optimization
Try and Error method

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-38703-6_16

Cite this

Wang, Z., & Sun, Y. (2013). Cask theory based parameter optimization for particle swarm optimization. In Advances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings (PART 1 ed., pp. 137-143). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7928 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-38703-6_16

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title = "Cask theory based parameter optimization for particle swarm optimization",

abstract = "To avoid the bored try and error method of finding a set of parameters of Particle Swarm Optimization (PSO) and achieve good optimization performance, it is desired to get an adaptive optimization method to search a good set of parameters. A nested optimization method is proposed in this paper and it can be used to search the tuned parameters such as inertia weight ω, acceleration coefficients c1 and c2, and so on. This method considers the cask theory to achieve a better optimization performance. Several famous benchmarks were used to validate the proposed method and the simulation results showed the efficiency of the proposed method.",

keywords = "Cask theory, Nested Optimization method, PSO, Parameter Optimization, Try and Error method",

author = "Zenghui Wang and Yanxia Sun",

year = "2013",

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language = "English",

isbn = "9783642387029",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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booktitle = "Advances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings",

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note = "4th International Conference on Advances in Swarm Intelligence, ICSI 2013 ; Conference date: 12-06-2012 Through 15-06-2012",

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Wang, Z & Sun, Y 2013, Cask theory based parameter optimization for particle swarm optimization. in Advances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 7928 LNCS, pp. 137-143, 4th International Conference on Advances in Swarm Intelligence, ICSI 2013, Harbin, China, 12/06/12. https://doi.org/10.1007/978-3-642-38703-6_16

Cask theory based parameter optimization for particle swarm optimization. / Wang, Zenghui; Sun, Yanxia.
Advances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings. PART 1. ed. 2013. p. 137-143 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7928 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Wang, Zenghui

AU - Sun, Yanxia

PY - 2013

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N2 - To avoid the bored try and error method of finding a set of parameters of Particle Swarm Optimization (PSO) and achieve good optimization performance, it is desired to get an adaptive optimization method to search a good set of parameters. A nested optimization method is proposed in this paper and it can be used to search the tuned parameters such as inertia weight ω, acceleration coefficients c1 and c2, and so on. This method considers the cask theory to achieve a better optimization performance. Several famous benchmarks were used to validate the proposed method and the simulation results showed the efficiency of the proposed method.

AB - To avoid the bored try and error method of finding a set of parameters of Particle Swarm Optimization (PSO) and achieve good optimization performance, it is desired to get an adaptive optimization method to search a good set of parameters. A nested optimization method is proposed in this paper and it can be used to search the tuned parameters such as inertia weight ω, acceleration coefficients c1 and c2, and so on. This method considers the cask theory to achieve a better optimization performance. Several famous benchmarks were used to validate the proposed method and the simulation results showed the efficiency of the proposed method.

KW - Cask theory

KW - Nested Optimization method

KW - PSO

KW - Parameter Optimization

KW - Try and Error method

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DO - 10.1007/978-3-642-38703-6_16

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Advances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings

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Wang Z, Sun Y. Cask theory based parameter optimization for particle swarm optimization. In Advances in Swarm Intelligence - 4th International Conference, ICSI 2013, Proceedings. PART 1 ed. 2013. p. 137-143. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-38703-6_16

Cask theory based parameter optimization for particle swarm optimization

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Keywords

ASJC Scopus subject areas

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