By Topic

Ant Colony Optimization

Cover Image Copyright Year: 2004
Author(s): Dorigo, M.; Stützle, T.
Publisher: MIT Press
Content Type : Books & eBooks
Topics: Computing & Processing (Hardware/Software)
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Abstract

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.

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      Front Matter

      Dorigo, M. ; Stützle, T.
      Ant Colony Optimization

      Page(s): i - xiv
      Copyright Year: 2004

      MIT Press eBook Chapters

      This chapter contains sections titled: Half Title, Title, Copyright, Dedication, Contents, Preface, Acknowledgments View full abstract»

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      From Real to Artificial Ants

      Dorigo, M. ; Stützle, T.
      Ant Colony Optimization

      Page(s): 1 - 24
      Copyright Year: 2004

      MIT Press eBook Chapters

      This chapter contains sections titled: Ants' Foraging Behavior and Optimization, Toward Artificial Ants, Artificial Ants and Minimum Cost Paths, Bibliographical Remarks, Things to Remember, Thought and Computer Exercises View full abstract»

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      The Ant Colony Optimization Metaheuristic

      Dorigo, M. ; Stützle, T.
      Ant Colony Optimization

      Page(s): 25 - 64
      Copyright Year: 2004

      MIT Press eBook Chapters

      This chapter contains sections titled: Combinatorial Optimization, The ACO Metaheuristic, How Do I Apply ACO?, Other Metaheuristics, Bibliographical Remarks, Things to Remember, Thought and Computer Exercises View full abstract»

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      Ant Colony Optimization Algorithms for the Traveling Salesman Problem

      Dorigo, M. ; Stützle, T.
      Ant Colony Optimization

      Page(s): 65 - 119
      Copyright Year: 2004

      MIT Press eBook Chapters

      This chapter contains sections titled: The Traveling Salesman Problem, ACO Algorithms for the TSP, Ant System and Its Direct Successors, Extensions of Ant System, Parallel Implementations, Experimental Evaluation, ACO plus Local Search, Implementing ACO Algorithms, Bibliographical Remarks, Things to Remember, Computer Exercises View full abstract»

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      Ant Colony Optimization Theory

      Dorigo, M. ; Stützle, T.
      Ant Colony Optimization

      Page(s): 121 - 152
      Copyright Year: 2004

      MIT Press eBook Chapters

      This chapter contains sections titled: Theoretical Considerations on ACO, The Problem and the Algorithm, Convergence Proofs, ACO and Model-Based Search, Bibliographical Remarks, Things to Remember, Thought and Computer Exercises View full abstract»

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      Ant Colony Optimization for NP-Hard Problems

      Dorigo, M. ; Stützle, T.
      Ant Colony Optimization

      Page(s): 153 - 222
      Copyright Year: 2004

      MIT Press eBook Chapters

      This chapter contains sections titled: Routing Problems, Assignment Problems, Scheduling Problems, Subset Problems, Application of ACO to Other NP-Hard Problems, Machine Learning Problems, Application Principles of ACO, Bibliographical Remarks, Things to Remember, Computer Exercises View full abstract»

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      AntNet: An ACO Algorithm for Data Network Routing

      Dorigo, M. ; Stützle, T.
      Ant Colony Optimization

      Page(s): 223 - 260
      Copyright Year: 2004

      MIT Press eBook Chapters

      This chapter contains sections titled: The Routing Problem, The AntNet Algorithm, The Experimental Settings, Results, AntNet and Stigmergy, AntNet, Monte Carlo Simulation, and Reinforcement Learning, Bibliographical Remarks, Things to Remember, Computer Exercises View full abstract»

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      Conclusions and Prospects for the Future

      Dorigo, M. ; Stützle, T.
      Ant Colony Optimization

      Page(s): 261 - 274
      Copyright Year: 2004

      MIT Press eBook Chapters

      This chapter contains sections titled: What Do We Know about ACO?, Current Trends in ACO, Ant Algorithms View full abstract»

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      Sources of Information about the ACO Field

      Dorigo, M. ; Stützle, T.
      Ant Colony Optimization

      Page(s): 275
      Copyright Year: 2004

      MIT Press eBook Chapters

      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. View full abstract»

    • Full text access may be available. Click article title to sign in or learn about subscription options.

      References

      Dorigo, M. ; Stützle, T.
      Ant Colony Optimization

      Page(s): 277 - 300
      Copyright Year: 2004

      MIT Press eBook Chapters

      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. View full abstract»

    • Full text access may be available. Click article title to sign in or learn about subscription options.

      Index

      Dorigo, M. ; Stützle, T.
      Ant Colony Optimization

      Page(s): 301 - 305
      Copyright Year: 2004

      MIT Press eBook Chapters

      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. View full abstract»