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

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. This chapter appears in the book, Recent Developments in Biologically Inspired Computing, edited by Leandro N. de Castro and Fernando J. Von Zuben. Copyright © 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.idea-group.com IDEA GROUP PUBLISHING 208 Sherafat, Nunes de Castro & Hruschka Copyright © 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited. INTRODUCTION AND MOTIVATION Providing efficient and feasible solutions to complex problems are some of the central challenges in engineering and computing. Finding out new approaches to solve well-known and novel problems is one of the main goals of these areas. From a biological perspective, it is also possible to view each form of life in nature as different “solutions” to the “problem” of surviving (Bonabeau et al., 2000): living organisms compete for limited resources and mating, and adapt themselves to changes in their environment. Therefore, living (surviving) is one of the most successful problem-solving techniques developed to date. In many cases, organic processes solve complex problems with simple elements; the interaction (cooperation, competition, and coordination) of the elements of the system exerts a more important role than the actions of individual agents themselves. By observing the behavior of a whole ant colony while dealing with several new and unexpected situations, it is possible to have an idea of the variability of emergent behaviors that can arise out of a group of simple agents. Ant colonies can form extremely complex raids in the search for food; they can build bridges with their own bodies in order to carry food over a valley; they can organize and clean their nests and create cemeteries; they can build nests with complicated tunnel and temperature control systems; and so forth. The study of the behavior of various insects seems to be a good approach to the understanding of some collective phenomena. It also gives us new insights into the development of novel problem solving techniques. Some insects can be particularly more adequate as a source of inspiration because they can be studied and observed without much complication and costs. In this sense, ants are specially relevant due to the large amount of information, both theoretical models and empirical observations, about their behavior. Several papers, books, and essays have already been published and may serve as a base for a bio-inspired problem-oriented study of specific phenomena (Camazine et al., 2001; Franks, 1989; Holldöbler & Wilson, 1994). However, ant colonies are not the only example of the successful collective behavior of simple organisms. A large group of termites working, a colony of bees searching for pollen, and a flock of birds flying synchronously are further examples. The dynamic and harmonic unity is one common element in all these collective behaviors and a factor whose implications are not well known. There is a lot of knowledge about each part of these (organic) systems; however, an understanding of the interactions of these elements, their integration and coordination, is not always available. Despite that, several researchers from computer science, engineering, and other fields of investigation have been developing tools for solving problems in the most varied domains, motivated by the collective behavior of simple social organisms. One term that has recently been proposed to characterize such systems is swarm systems or swarm intelligence (Bonabeau et al., 1999; Kennedy et al., 2001). Algorithms inspired by the collective behavior of diverse groups of animals and insect societies have resulted in new computational tools with applications in areas such as: • Planning and task allocation (Bonabeau & Théraulaz, 2000); • Robotics (Kube & Bonabeau, 2000); 26 more pages are available in the full version of this document, which may be purchased using the "Add to Cart" button on the product's webpage: www.igi-global.com/chapter/influence-pheromone-adaptivevision-standard/28329? camid=4v1 This title is available in InfoSci-Books, InfoSci-Medical, Communications, Social Science, and Healthcare. Recommend this product to your librarian: www.igi-global.com/e-resources/libraryrecommendation/? id=1