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During the last decade, artificial ants have experienced rapid development in the research community, mainly for solving optimization problems. This book provides an overview of the situation ant colony algorithms reached. Artificial Ants encompasses solution methods of hard optimization problems and new trends for collective intelligence. Part 1 helps to understand the basis of ant colony algorithms, and to discover a panorama of applications in the field of optimization, particularly in the industrial world. Part 2 deals with broader issues and provides an overview of current research in the field of artificial ants.
An overview of the rapidly growing field of ant colony optimization that describes theoretical findings, the major algorithms, and current applications. The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses. The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms.
This book offers a basic introduction to genetic algorithms. It provides a detailed explanation of genetic algorithm concepts and examines numerous genetic algorithm optimization problems. In addition, the book presents implementation of optimization problems using C and C++ as well as simulated solutions for genetic algorithm problems using MATLAB 7.0. It also includes application case studies on genetic algorithms in emerging fields.
This book constitutes the refereed proceedings of the 5th International Workshop on Ant Colony Optimization and Swarm Intelligence, ANTS 2006, held in Brussels, Belgium, in September 2006. The 27 revised full papers, 23 revised short papers, and 12 extended abstracts presented were carefully reviewed and selected from 115 submissions.
An exploration of the implications of developments in artificial intelligence for social scientific research, which builds on the theoretical and methodological insights provided by "Simulating societies".; This book is intended for worldwide library market for social science subjects such as sociology, political science, geography, archaeology/anthropology, and significant appeal within computer science, particularly artificial intelligence. Also personal reference for researchers.
Ants are legion: at present there are 11,006 species of ant known; they live everywhere in the world except the polar icecaps; and the combined weight of the ant population has been estimated to make up half the mass of all insects alive today. When we encounter them outdoors, ants fascinate us; discovered in our kitchen cupboards, they elicit horror and disgust. Charlotte Sleigh’s Ant elucidates the cultural reasons behind our varied reactions to these extraordinary insects, and considers the variety of responses that humans have expressed at different times and in different places to their intricate, miniature societies. Ants have figured as fantasy miniature armies, as models of good behavior, as infiltrating communists and as creatures on the borderline between the realms of the organic and the machine: in 1977 British Telecom hired ant experts to help solve problems with their massive information network. This is the first book to examine ants in these and many other such guises, and in so doing opens up broader issues about the history of science and humans’ relations with the natural world. It will be of interest to anyone who likes natural history or cultural studies, or who has ever rushed out and bought a can of RaidTM. "[Charlotte Sleigh's] stylish, engaging and informative study deserves to win new members for the ant fan club."—Jonathan Bate, The Times
This invaluable book is the first of its kind on 'selforganizology', the science of self-organization. It covers a wide range of topics, such as the theory, principle and methodology of selforganizology, agent-based modelling, intelligence basis, ant colony optimization, fish/particle swarm optimization, cellular automata, spatial diffusion models, evolutionary algorithms, self-adaptation and control systems, self-organizing neural networks, catastrophe theory and methods, and self-organization of biological communities, etc.Readers will have an in-depth and comprehensive understanding of selforganizology, with detailed background information provided for those who wish to delve deeper into the subject and explore research literature.This book is a valuable reference for research scientists, university teachers, graduate students and high-level undergraduates in the areas of computational science, artificial intelligence, applied mathematics, engineering science, social science and life sciences.
The behavior of real ants motivated Dorigo et al. [DMC96] to propose the Ant Colony Optimization (ACO) technique, which can be used to solve problems in dynamic environments. This technique has been successfully applied to several optimization problems [FMS05, PB05, BN06, SF06, PLF02, WGDK06, CF06, HND05]. Such results have motivated this chapter which presents ACO concepts, case studies and also a complete example on process scheduling optimization. Besides the successful adoption of ACO, it presents some relevant questions which have been motivating future directions such as: how to adjust parameters which depend on the optimization problem [SocOSj; how to reduce the execution time [G.N06, MBSD06]; the optimization improvement by using incremental local search [BBSD06]; and the aggregation of different and new concepts to ACO [RL04]. Those works confirm ACO is an important optimization technique and also that is has been improved and present a promising future.
This book is about synergy in computational intelligence (CI). It is a c- lection of chapters that covers a rich and diverse variety of computer-based techniques, all involving some aspect of computational intelligence, but each one taking a somewhat pragmatic view. Many complex problems in the real world require the application of some form of what we loosely call “intel- gence”fortheirsolution. Fewcanbesolvedbythenaiveapplicationofasingle technique, however good it is. Authors in this collection recognize the li- tations of individual paradigms, and propose some practical and novel ways in which di?erent CI techniques can be combined with each other, or with more traditional computational techniques, to produce powerful probl- solving environments which exhibit synergy, i. e. , systems in which the whole 1 is greater than the sum of the parts . Computational intelligence is a relatively new term, and there is some d- agreement as to its precise de?nition. Some practitioners limit its scope to schemes involving evolutionary algorithms, neural networks, fuzzy logic, or hybrids of these. For others, the de?nition is a little more ?exible, and will include paradigms such as Bayesian belief networks, multi-agent systems, case-based reasoning and so on. Generally, the term has a similar meaning to the well-known phrase “Arti?cial Intelligence” (AI), although CI is p- ceived moreas a “bottom up” approachfrom which intelligent behaviour can emerge,whereasAItendstobestudiedfromthe“topdown”,andderivefrom pondering upon the “meaning of intelligence”. (These and other key issues will be discussed in more detail in Chapter 1.