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The Influence of Pheromone and Adaptive Vision in the Standard Ant Clustering Algorithm

The Influence of Pheromone and Adaptive Vision in the Standard Ant Clustering Algorithm

Vahid Sherafat, Leandro Nunes de Castro, Eduardo Raul Hruschka
Copyright: © 2005 |Pages: 28
ISBN13: 9781591403128|ISBN10: 159140312X|ISBN13 Softcover: 9781591403135|EISBN13: 9781591403142
DOI: 10.4018/978-1-59140-312-8.ch009
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MLA

Sherafat, Vahid, et al. "The Influence of Pheromone and Adaptive Vision in the Standard Ant Clustering Algorithm." Recent Developments in Biologically Inspired Computing, edited by Leandro Nunes de Castro and Fernando J. Von Zuben, IGI Global, 2005, pp. 207-234. https://doi.org/10.4018/978-1-59140-312-8.ch009

APA

Sherafat, V., de Castro, L. N., & Hruschka, E. R. (2005). The Influence of Pheromone and Adaptive Vision in the Standard Ant Clustering Algorithm. In L. Nunes de Castro & F. Von Zuben (Eds.), Recent Developments in Biologically Inspired Computing (pp. 207-234). IGI Global. https://doi.org/10.4018/978-1-59140-312-8.ch009

Chicago

Sherafat, Vahid, Leandro Nunes de Castro, and Eduardo Raul Hruschka. "The Influence of Pheromone and Adaptive Vision in the Standard Ant Clustering Algorithm." In Recent Developments in Biologically Inspired Computing, edited by Leandro Nunes de Castro and Fernando J. Von Zuben, 207-234. Hershey, PA: IGI Global, 2005. https://doi.org/10.4018/978-1-59140-312-8.ch009

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Abstract

Algorithms inspired by the collective behavior of social organisms, from insect colonies to human societies, promoted the emergence of a new field of research called swarm intelligence. The applications of swarm intelligence range from routing in telecommunication networks to robotics. This chapter discusses some of the ideas behind swarm intelligence, focusing on a clustering algorithm motivated by the social behavior of some ant species. The standard ant-clustering algorithm is presented; a brief review from the literature concerning the applications and variations of the basic model is provided; two novel modifications of the original algorithm are proposed and discussed; and a sensitivity analysis of the standard and modified algorithm in relation to some user-defined parameters is performed. A variation of a simple benchmark problem in the field is used to perform the sensitivity analysis of the algorithm and to assess the proposed modifications of the standard algorithm.

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