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From its opening pages, Ant Colony immerses the reader in a world that is darkly existential, with false prophets, unjust wars, and corrupt police officers, as it follows the denizens of a black ant colony under attack from the nearby red ants. On the surface, it's the story of this war, the destruction of a civilization, and the ants' all too familiar desire to rebuild. Underneath, though, Ant Colony plumbs the deepest human concerns--loneliness, faith, love, apathy, and more. All of this is done with humor and sensitivity, exposing a world where spiders can wreak unimaginable amounts of havoc with a single gnash of their jaws.
Ant Colony Optimization by Marco Dorigo,Thomas Stutzle Pdf
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.
An unprecedented look at the complex and beautiful world of underground ant architecture Walter Tschinkel has spent much of his career investigating the hidden subterranean realm of ant nests. This wonderfully illustrated book takes you inside an unseen world where thousands of ants build intricate homes in the soil beneath our feet. Tschinkel describes the ingenious methods he has devised to study ant nests, showing how he fills a nest with plaster, molten metal, or wax and painstakingly excavates the cast. He guides you through living ant nests chamber by chamber, revealing how nests are created and how colonies function. How does nest architecture vary across species? Do ants have "architectural plans"? How do nests affect our environment? As he delves into these and other questions, Tschinkel provides a one-of-a-kind natural history of the planet's most successful creatures and a compelling firsthand account of a life of scientific discovery. Offering a unique look at how simple methods can lead to pioneering science, Ant Architecture addresses the unsolved mysteries of underground ant nests while charting new directions for tomorrow’s research, and reflects on the role of beauty in nature and the joys of shoestring science.
The mighty ant has been around since the age of dinosaurs. Readers will discover how ants work as a team and why they've been able to survive so long as they explore an ant colony.
How do ant colonies get anything done, when no one is in charge? An ant colony operates without a central control or hierarchy, and no ant directs another. Instead, ants decide what to do based on the rate, rhythm, and pattern of individual encounters and interactions--resulting in a dynamic network that coordinates the functions of the colony. Ant Encounters provides a revealing and accessible look into ant behavior from this complex systems perspective. Focusing on the moment-to-moment behavior of ant colonies, Deborah Gordon investigates the role of interaction networks in regulating colony behavior and relations among ant colonies. She shows how ant behavior within and between colonies arises from local interactions of individuals, and how interaction networks develop as a colony grows older and larger. The more rapidly ants react to their encounters, the more sensitively the entire colony responds to changing conditions. Gordon explores whether such reactive networks help a colony to survive and reproduce, how natural selection shapes colony networks, and how these structures compare to other analogous complex systems. Ant Encounters sheds light on the organizational behavior, ecology, and evolution of these diverse and ubiquitous social insects.
Ants communicate information by leaving pheromone tracks. A moving ant leaves, in varying quantities, some pheromone on the ground to mark its way. While an isolated ant moves essentially at random, an ant encountering a previously laid trail is able to detect it and decide with high probability to follow it, thus reinforcing the track with its own pheromone. The collective behavior that emerges is thus a positive feedback: where the more the ants following a track, the more attractive that track becomes for being followed; thus the probability with which an ant chooses a path increases with the number of ants that previously chose the same path. This elementary ant's behavior inspired the development of ant colony optimization by Marco Dorigo in 1992, constructing a meta-heuristic stochastic combinatorial computational methodology belonging to a family of related meta-heuristic methods such as simulated annealing, Tabu search and genetic algorithms. This book covers in twenty chapters state of the art methods and applications of utilizing ant colony optimization algorithms. New methods and theory such as multi colony ant algorithm based upon a new pheromone arithmetic crossover and a repulsive operator, new findings on ant colony convergence, and a diversity of engineering and science applications from transportation, water resources, electrical and computer science disciplines are presented.
Ant Colony Optimization (ACO) is the best example of how studies aimed at understanding and modeling the behavior of ants and other social insects can provide inspiration for the development of computational algorithms for the solution of difficult mathematical problems. Introduced by Marco Dorigo in his PhD thesis (1992) and initially applied to the travelling salesman problem, the ACO field has experienced a tremendous growth, standing today as an important nature-inspired stochastic metaheuristic for hard optimization problems. This book presents state-of-the-art ACO methods and is divided into two parts: (I) Techniques, which includes parallel implementations, and (II) Applications, where recent contributions of ACO to diverse fields, such as traffic congestion and control, structural optimization, manufacturing, and genomics are presented.
Ant Colony Optimization and Swarm Intelligence by Directeur de Recherches Du Fnrs Marco Dorigo,Marco Dorigo,Mauro Birattari,Christian Blum,Luca M. Gambardella,Francesco Mondada,Thomas Stützle Pdf
This book constitutes the refereed proceedings of the 4th International Workshop on Ant Colony Optimization and Swarm Intelligence, ANTS 2004, held in Brussels, Belgium in September 2004. The 22 revised full papers, 19 revised short papers, and 9 poster abstracts presented were carefully reviewed and selected from 79 papers submitted. The papers are devoted to theoretical and foundational aspects of ant algorithms, ant colony optimization and swarm intelligence and deal with a broad variety of optimization applications in networking and operations research.
Ant Colony Optimization Algorithms by Fouad Sabry Pdf
What Is Ant Colony Optimization Algorithms The Ant Colony Optimization Algorithm, also known as ACO, is a probabilistic technique for addressing computational problems in the fields of computer science and operations research. These problems can be boiled down to the task of finding good paths through graphs. The behavior of natural ants served as inspiration for the development of multi-agent systems, which are represented by artificial ants. The communication of biological ants through the use of pheromones is frequently the major paradigm that is adopted. Combinations of artificial ants and local search algorithms have become the technique of choice for several optimization tasks involving some kind of graph, such as internet routing and vehicle routing. This is because these combinations are able to find optimal solutions more quickly than traditional methods. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Ant colony optimization algorithms Chapter 2: Job-shop scheduling Chapter 3: Open-shop scheduling Chapter 4: Quadratic assignment problem Chapter 5: Generalized assignment problem Chapter 6: Set cover problem Chapter 7: Partition problem Chapter 8: Bankruptcy prediction Chapter 9: Protein-protein interaction Chapter 10: Protein folding (II) Answering the public top questions about ant colony optimization algorithms. (III) Real world examples for the usage of ant colony optimization algorithms in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of ant colony optimization algorithms. What is Artificial Intelligence Series The artificial intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.
If a second-grader was a strong as an ant, he or she would be able to lift a car. This book takes readers under the surface into the fascinating and complex world of the ant colony. Books of the Real Life Readers Program use real life scenario narratives to help readers further develop content-area reading, writing, and comprehension skills.
Ant Colony Optimization and Constraint Programming by Christine Solnon Pdf
Ant colony optimization is a metaheuristic which has beensuccessfully applied to a wide range of combinatorial optimizationproblems. The author describes this metaheuristic and studies itsefficiency for solving some hard combinatorial problems, with aspecific focus on constraint programming. The text is organizedinto three parts. The first part introduces constraint programming, which provideshigh level features to declaratively model problems by means ofconstraints. It describes the main existing approaches for solvingconstraint satisfaction problems, including complete tree searchapproaches and metaheuristics, and shows how they can be integratedwithin constraint programming languages. The second part describes the ant colony optimizationmetaheuristic and illustrates its capabilities on differentconstraint satisfaction problems. The third part shows how the ant colony may be integrated within aconstraint programming language, thus combining the expressivepower of constraint programming languages, to describe problems ina declarative way, and the solving power of ant colony optimizationto efficiently solve these problems.
Ant Colony Optimization and Applications by Stefka Fidanova Pdf
This book is interesting and full of new ideas. It provokes the curiosity of the readers. The book targets both researchers and practitioners. The students and the researchers will acquire knowledge about ant colony optimization and its possible applications as well as practitioners will find new ideas and solutions of their combinatorial optimization and decision-making problems. Ant colony optimization is between the best method for solving difficult optimization problems arising in real life and industry. It has obtained distinguished results on some applications with very restrictive constraints. The reader will find theoretical aspects of ant method as well as applications on a variety of problems. The following applications could be mentioned: multiple knapsack problem, which is an important economical problem; grid scheduling problem; GPS surveying problem; E. coli cultivation modeling; wireless sensor network positioning; image edges detection; workforce planning.
Decision Tree and Ensemble Learning Based on Ant Colony Optimization by Jan Kozak Pdf
This book not only discusses the important topics in the area of machine learning and combinatorial optimization, it also combines them into one. This was decisive for choosing the material to be included in the book and determining its order of presentation. Decision trees are a popular method of classification as well as of knowledge representation. At the same time, they are easy to implement as the building blocks of an ensemble of classifiers. Admittedly, however, the task of constructing a near-optimal decision tree is a very complex process. The good results typically achieved by the ant colony optimization algorithms when dealing with combinatorial optimization problems suggest the possibility of also using that approach for effectively constructing decision trees. The underlying rationale is that both problem classes can be presented as graphs. This fact leads to option of considering a larger spectrum of solutions than those based on the heuristic. Moreover, ant colony optimization algorithms can be used to advantage when building ensembles of classifiers. This book is a combination of a research monograph and a textbook. It can be used in graduate courses, but is also of interest to researchers, both specialists in machine learning and those applying machine learning methods to cope with problems from any field of R&D.