Comparison between ant colony and genetic algorithms for fuzzy system optimization

Cristina Martinez; Oscar Castillo; Oscar Montiel

doi:10.1007/978-3-540-70812-4_5

Comparison between ant colony and genetic algorithms for fuzzy system optimization

Cristina Martinez, Oscar Castillo, Oscar Montiel

Centro de Investigación y Desarrollo de Tecnología Digital (CITEDI)

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

20 Scopus citations

Abstract

In this paper we show some of the results that we obtain with different evolutionary methods on a Mamdani Fuzzy Inference System (FIS); we work with Hierarchical Genetic Algorithms (HGA) and the Ant Colony Optimization (ACO), the fuzzy inference system controls a benchmark problem which is "The Ball and Beam" system, optimizing the fuzzy rules of the system. Firs, we work to optimize the FIS that is structured by two inputs (the error and the derived error), an output (the angle of the beam so that we can get the ball position on it); and the 44 fuzzy rules that we used to be reduced with the evolutionary methods (HGA, ACO), so that we could make the comparisons between them via average and standard deviation, and concluding with the best evolutionary method for a fuzzy system optimization control problem.

Original language	English
Title of host publication	Soft Computing for Hybrid Intelligent Systems
Editors	Oscar Castillo, Patricia Melin, Janusz Kacprzyk, Witold Pedrycz
Pages	71-86
Number of pages	16
DOIs	https://doi.org/10.1007/978-3-540-70812-4_5
State	Published - 2008

Publication series

Name	Studies in Computational Intelligence
Volume	154
ISSN (Print)	1860-949X

Access to Document

10.1007/978-3-540-70812-4_5

Cite this

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Comparison between ant colony and genetic algorithms for fuzzy system optimization. / Martinez, Cristina; Castillo, Oscar; Montiel, Oscar.
Soft Computing for Hybrid Intelligent Systems. ed. / Oscar Castillo; Patricia Melin; Janusz Kacprzyk; Witold Pedrycz. 2008. p. 71-86 (Studies in Computational Intelligence; Vol. 154).

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

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Comparison between ant colony and genetic algorithms for fuzzy system optimization

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