A comparative analysis of FSS with CMA-ES and S-PSO in ill-conditioned problems

Anthony J. Anthony; Fernando B. Lima-Neto; François Fages; Carmelo J.A. Bastos-Filho

doi:10.1007/978-3-642-32639-4_51

A comparative analysis of FSS with CMA-ES and S-PSO in ill-conditioned problems

Anthony J. Anthony, Fernando B. Lima-Neto, François Fages, Carmelo J.A. Bastos-Filho

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

Abstract

This paper presents a comparative analyzes between three search algorithms, named Fish School Search, Particle Swarm Optimization and Covariance Matrix Adaptation Evolution Strategy applied to ill-conditioned problems. We aim to demonstrate the effectiveness of the Fish School Search in the optimization processes when the objective function has ill-conditioned properties. We achieved good results for the Fish School Search and in some cases we obtained superior results when compared to the other algorithms.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning, IDEAL 2012 - 13th International Conference, Proceedings
Pages	416-422
Number of pages	7
DOIs	https://doi.org/10.1007/978-3-642-32639-4_51
Publication status	Published - 20 Aug 2012
Externally published	Yes
Event	13th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2012 - Natal, Brazil Duration: 29 Aug 2012 → 31 Aug 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7435 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2012
Country/Territory	Brazil
City	Natal
Period	29/08/12 → 31/08/12

Keywords

Covariance matrix adaptation
Fish School Search
Ill-conditioned problems
Invariance
Non-separable problems
Particle Swarm Optimization

Access to Document

10.1007/978-3-642-32639-4_51

Cite this

Anthony, A. J., Lima-Neto, F. B., Fages, F., & Bastos-Filho, C. J. A. (2012). A comparative analysis of FSS with CMA-ES and S-PSO in ill-conditioned problems. In Intelligent Data Engineering and Automated Learning, IDEAL 2012 - 13th International Conference, Proceedings (pp. 416-422). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7435 LNCS). https://doi.org/10.1007/978-3-642-32639-4_51

Anthony, Anthony J. ; Lima-Neto, Fernando B. ; Fages, François et al. / A comparative analysis of FSS with CMA-ES and S-PSO in ill-conditioned problems. Intelligent Data Engineering and Automated Learning, IDEAL 2012 - 13th International Conference, Proceedings. 2012. pp. 416-422 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "A comparative analysis of FSS with CMA-ES and S-PSO in ill-conditioned problems",

abstract = "This paper presents a comparative analyzes between three search algorithms, named Fish School Search, Particle Swarm Optimization and Covariance Matrix Adaptation Evolution Strategy applied to ill-conditioned problems. We aim to demonstrate the effectiveness of the Fish School Search in the optimization processes when the objective function has ill-conditioned properties. We achieved good results for the Fish School Search and in some cases we obtained superior results when compared to the other algorithms.",

keywords = "Covariance matrix adaptation, Fish School Search, Ill-conditioned problems, Invariance, Non-separable problems, Particle Swarm Optimization",

author = "Anthony, \{Anthony J.\} and Lima-Neto, \{Fernando B.\} and Fran{\c c}ois Fages and Bastos-Filho, \{Carmelo J.A.\}",

year = "2012",

month = aug,

day = "20",

doi = "10.1007/978-3-642-32639-4\_51",

language = "English",

isbn = "9783642326387",

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

pages = "416--422",

booktitle = "Intelligent Data Engineering and Automated Learning, IDEAL 2012 - 13th International Conference, Proceedings",

note = "13th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2012 ; Conference date: 29-08-2012 Through 31-08-2012",

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Anthony, AJ, Lima-Neto, FB, Fages, F & Bastos-Filho, CJA 2012, A comparative analysis of FSS with CMA-ES and S-PSO in ill-conditioned problems. in Intelligent Data Engineering and Automated Learning, IDEAL 2012 - 13th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7435 LNCS, pp. 416-422, 13th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2012, Natal, Brazil, 29/08/12. https://doi.org/10.1007/978-3-642-32639-4_51

A comparative analysis of FSS with CMA-ES and S-PSO in ill-conditioned problems. / Anthony, Anthony J.; Lima-Neto, Fernando B.; Fages, François et al.
Intelligent Data Engineering and Automated Learning, IDEAL 2012 - 13th International Conference, Proceedings. 2012. p. 416-422 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7435 LNCS).

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

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AB - This paper presents a comparative analyzes between three search algorithms, named Fish School Search, Particle Swarm Optimization and Covariance Matrix Adaptation Evolution Strategy applied to ill-conditioned problems. We aim to demonstrate the effectiveness of the Fish School Search in the optimization processes when the objective function has ill-conditioned properties. We achieved good results for the Fish School Search and in some cases we obtained superior results when compared to the other algorithms.

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KW - Invariance

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KW - Particle Swarm Optimization

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Anthony AJ, Lima-Neto FB, Fages F, Bastos-Filho CJA. A comparative analysis of FSS with CMA-ES and S-PSO in ill-conditioned problems. In Intelligent Data Engineering and Automated Learning, IDEAL 2012 - 13th International Conference, Proceedings. 2012. p. 416-422. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-32639-4_51

A comparative analysis of FSS with CMA-ES and S-PSO in ill-conditioned problems

Abstract

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Keywords

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