Ant Colony Optimization Algorithms
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Ant Colony Optimization
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Author : Avi Ostfeld
language : en
Publisher: BoD – Books on Demand
Release Date : 2011-02-04
Ant Colony Optimization written by Avi Ostfeld and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-02-04 with Computers categories.
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
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Author : Helio Barbosa
language : en
Publisher: BoD – Books on Demand
Release Date : 2013-02-20
Ant Colony Optimization written by Helio Barbosa and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-02-20 with Computers categories.
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 Algorithms
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Author : Fouad Sabry
language : en
Publisher: One Billion Knowledgeable
Release Date : 2023-07-01
Ant Colony Optimization Algorithms written by Fouad Sabry and has been published by One Billion Knowledgeable this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-01 with Computers categories.
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.
Ant Colony Optimization Algorithms
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Author : Gerard Blokdyk
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-11-21
Ant Colony Optimization Algorithms written by Gerard Blokdyk and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-21 with categories.
What other areas of the organization might benefit from the Ant colony optimization algorithms team's improvements, knowledge, and learning? Whats the best design framework for Ant colony optimization algorithms organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant? What are the Key enablers to make this Ant colony optimization algorithms move? Does Ant colony optimization algorithms analysis isolate the fundamental causes of problems? Is the Ant colony optimization algorithms process severely broken such that a re-design is necessary? Defining, designing, creating, and implementing a process to solve a business challenge or meet a business objective is the most valuable role... In EVERY company, organization and department. Unless you are talking a one-time, single-use project within a business, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' This Self-Assessment empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make Ant colony optimization algorithms investments work better. This Ant colony optimization algorithms All-Inclusive Self-Assessment enables You to be that person. All the tools you need to an in-depth Ant colony optimization algorithms Self-Assessment. Featuring 697 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Ant colony optimization algorithms improvements can be made. In using the questions you will be better able to: - diagnose Ant colony optimization algorithms projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices - implement evidence-based best practice strategies aligned with overall goals - integrate recent advances in Ant colony optimization algorithms and process design strategies into practice according to best practice guidelines Using a Self-Assessment tool known as the Ant colony optimization algorithms Scorecard, you will develop a clear picture of which Ant colony optimization algorithms areas need attention. Your purchase includes access details to the Ant colony optimization algorithms self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. Your exclusive instant access details can be found in your book.
Ant Colony Optimization
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Author : Marco Dorigo
language : en
Publisher: MIT Press
Release Date : 2004-06-04
Ant Colony Optimization written by Marco Dorigo and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-06-04 with Computers categories.
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.
Ant Colony Optimization
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Author : Avi Ostfeld
language : en
Publisher: IntechOpen
Release Date : 2011-02-04
Ant Colony Optimization written by Avi Ostfeld and has been published by IntechOpen this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-02-04 with Computers categories.
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 And Swarm Intelligence
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Author : Marco Dorigo
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-09-10
Ant Colony Optimization And Swarm Intelligence written by Marco Dorigo and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-09-10 with Computers categories.
The series of biannual international conferences “ANTS – International C- ference on Ant Colony Optimization and Swarm Intelligence”, now in its sixth edition, was started ten years ago, with the organization of ANTS’98. As some readers might recall, the ?rst edition of ANTS was titled “ANTS’98 – From Ant Colonies to Arti?cial Ants: First International Workshop on Ant Colony Op- mization. ” In fact, at that time the focus was mainly on ant colony optimization (ACO), the ?rst swarm intelligence algorithm to go beyond a pure scienti?c interest and to enter the realm of real-world applications. Interestingly, in the ten years after the ?rst edition there has been a gr- ing interest not only for ACO, but for a number of other studies that belong more generally to the area of swarmintelligence. The rapid growth of the swarm intelligence ?eld is attested by a number of indicators. First, the number of s- missions and participants to the ANTS conferences has steadily increased over the years. Second, a number of international conferences in computational - telligence and related disciplines organize workshops on subjects such as swarm intelligence, ant algorithms, ant colony optimization, and particle swarm op- mization. Third, IEEE startedorganizing,in 2003,the IEEE SwarmIntelligence Symposium (in order to maintain unity in this growing ?eld, we are currently establishingacooperationagreementbetweenIEEE SISandANTSsoastohave 1 IEEE SIS in odd years and ANTS in even years). Last, the Swarm Intelligence journal was born.
Ant Colony Optimization Algorithms
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Author : Gerardus Blokdyk
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2018-01-13
Ant Colony Optimization Algorithms written by Gerardus Blokdyk and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-13 with categories.
What other areas of the organization might benefit from the Ant colony optimization algorithms team's improvements, knowledge, and learning? Whats the best design framework for Ant colony optimization algorithms organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant? What are the Key enablers to make this Ant colony optimization algorithms move? Does Ant colony optimization algorithms analysis isolate the fundamental causes of problems? Is the Ant colony optimization algorithms process severely broken such that a re-design is necessary? Defining, designing, creating, and implementing a process to solve a business challenge or meet a business objective is the most valuable role... In EVERY company, organization and department. Unless you are talking a one-time, single-use project within a business, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' This Self-Assessment empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make Ant colony optimization algorithms investments work better. This Ant colony optimization algorithms All-Inclusive Self-Assessment enables You to be that person. All the tools you need to an in-depth Ant colony optimization algorithms Self-Assessment. Featuring 697 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Ant colony optimization algorithms improvements can be made. In using the questions you will be better able to: - diagnose Ant colony optimization algorithms projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices - implement evidence-based best practice strategies aligned with overall goals - integrate recent advances in Ant colony optimization algorithms and process design strategies into practice according to best practice guidelines Using a Self-Assessment tool known as the Ant colony optimization algorithms Scorecard, you will develop a clear picture of which Ant colony optimization algorithms areas need attention. Your purchase includes access details to the Ant colony optimization algorithms self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. Your exclusive instant access details can be found in your book.
Ant Colony Optimization Algorithm For Load Balancing In Grid Computing Uum Press
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Author : Ku Ruhana Ku Mahamud
language : en
Publisher: UUM Press
Release Date : 2012-01-01
Ant Colony Optimization Algorithm For Load Balancing In Grid Computing Uum Press written by Ku Ruhana Ku Mahamud and has been published by UUM Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-01-01 with Computers categories.
Managing resources in grid computing system is complicated due to the distributed and heterogeneous nature of the resources. This research proposes an enhancement of the ant colony optimization algorithm that caters for dynamic scheduling and load balancing in the grid computing system. The proposed algorithm is known as the enhance Ant Colony Optimization (EACO). The algorithm consists of three new mechanisms that organize the work of an ant colony i.e. initial pheromone value mechanism, resource selection mechanism and pheromone update mechanism. The resource allocation problem is modelled as a graph that can be used by the ant to deliver its pheromone. This graph consists of four types of vertices which are job, requirement, resource and capacity that are used in constructing the grid resource management element. The proposed EACO algorithm takes into consideration the capacity of resources and the characteristics of jobs in determining the best resource to process a job. EACO selects the resources based on the pheromone value on each resource which is recorded in a matrix form. The initial pheromone value of each resource for each job is calculated based on the estimated transmission time and execution time of a given job. Resources with high pheromone value are selected to process the submitted jobs. Global pheromone update is performed after the completion of processing the jobs in order to reduce the pheromone value of resources. A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against other ant based algorithm, in terms of resource utilization. Experimental results show that EACO produced better grid resource management solution.
Ant Colony Optimization And Applications
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Author : Stefka Fidanova
language : en
Publisher: Springer Nature
Release Date : 2021-02-27
Ant Colony Optimization And Applications written by Stefka Fidanova and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-27 with Technology & Engineering categories.
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.