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Ant Colony Optimization

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Christian Blum – 1st expert on this subject based on the ideXlab platform

  • Ant Colony Optimization theory: A survey
    Theoretical Computer Science, 2020
    Co-Authors: Marco Dorigo, Christian Blum

    Abstract:

    Research on a new metaheuristic for Optimization is often initially focused on proof-of-concept applications. It is only after experimental work has shown the practical interest of the method that researchers try to deepen their understanding of the method’s functioning not only through more and more sophisticated experiments but also by means of an effort to build a theory. Tackling questions such as “how and why the method works” is importAnt, because finding an answer may help in improving its applicability. Ant Colony Optimization, which was introduced in the early 1990s as a novel technique for solving hard combinatorial Optimization problems, finds itself currently at this point of its life cycle. With this article we provide a survey on theoretical results on Ant Colony Optimization. First, we review some convergence results. Then we discuss relations between Ant Colony Optimization algorithms and other approximate methods for Optimization. Finally, we focus on some research efforts directed at gaining a deeper understanding of the behavior of Ant Colony Optimization algorithms. Throughout the paper we identify some open questions with a certain interest of being solved in the near future. © 2005 Elsevier B.V. All rights reserved.SCOPUS: ar.jinfo:eu-repo/semAntics/publishe

  • HIS – Ant Colony Optimization: Introduction and Hybridizations
    7th International Conference on Hybrid Intelligent Systems (HIS 2007), 2007
    Co-Authors: Christian Blum

    Abstract:

    This paper contains complimentary material to the tutorial “Ant Colony Optimization: introduction and hybridizations” given by the author at HIS 2007, Kaiserslautern, Germany. First, Ant Colony Optimization is shortly introduced. Then, successful recent hybridizations of Ant Colony Optimization algorithms with other techniques for Optimization are reviewed.

  • Ant Colony Optimization: Introduction and hybridizations
    Proceedings – 7th International Conference on Hybrid Intelligent Systems HIS 2007, 2007
    Co-Authors: Christian Blum

    Abstract:

    Ant Colony Optimization is a technique for Optimization that was introduced in the early 1990’s. The inspiring source of Ant Colony Optimization is the foraging behavior of real Ant colonies. This behavior is exploited in artificial Ant colonies for the search of approximate solutions to discrete Optimization problems, to continuous Optimization problems, and to importAnt problems in telecommunications, such as routing and load balancing. First, we deal with the biological inspiration of Ant Colony Optimization algorithms. We show how this biological inspiration can be transfered into an algorithm for discrete Optimization. Then, we outline Ant Colony Optimization in more general terms in the context of discrete Optimization, and present some of the nowadays best-performing Ant Colony Optimization variAnts. After summarizing some importAnt theoretical results, we demonstrate how Ant Colony Optimization can be applied to continuous Optimization problems. Finally, we provide examples of an interesting recent research direction: The hybridization with more classical techniques from artificial intelligence and operations research. © 2005 Elsevier B.V. All rights reserved.

Thomas Stutzle – 2nd expert on this subject based on the ideXlab platform

  • A critical analysis of parameter adaptation in Ant Colony Optimization
    Swarm Intelligence, 2011
    Co-Authors: Paola Pellegrini, Thomas Stutzle, Mauro Birattari

    Abstract:

    Applying parameter adaptation means operating on parameters of an algorithm while it is tackling an instance. For Ant Colony Optimization, several parameter adaptation methods have been proposed. In the literature, these methods have been shown to improve the quality of the results achieved in some particular contexts. In particular, they proved to be successful when applied to novel Ant Colony Optimization algorithms for tackling problems that are not a classical testbed for Optimization algorithms. In this paper, we show that the adaptation methods proposed so far do not improve, and often even worsen the performance when applied to high performing Ant Colony Optimization algorithms for some classical combinatorial Optimization problems.

  • Ant Colony Optimization
    International Conference on Evolutionary Multi-criterion Optimization, 2009
    Co-Authors: Thomas Stutzle

    Abstract:

    Ant Colony Optimization (ACO) is a stochastic local search method that has been inspired by the pheromone trail laying and following behavior of some Ant species [1]. Artificial Ants in ACO essentially are randomized construction procedures that generate solutions based on (artificial) pheromone trails and heuristic information that are associated to solution components. Since the first ACO algorithm has been proposed in 1991, this algorithmic method has attracted a large number of researchers and in the meAntime it has reached a significAnt level of maturity. In fact, ACO is now a well-established search technique for tackling a wide variety of computationally hard problems.

  • Ant Colony Optimization artificial Ants as a computational intelligence technique
    IEEE Computational Intelligence Magazine, 2006
    Co-Authors: Marco Dorigo, Mauro Birattari, Thomas Stutzle

    Abstract:

    The introduction of Ant Colony Optimization (ACO) and to survey its most notable applications are discussed. Ant Colony Optimization takes inspiration from the forging behavior of some Ant species. These Ants deposit Pheromone on the ground in order to mark some favorable path that should be followed by other members of the Colony. The model proposed by Deneubourg and co-workers for explaining the foraging behavior of Ants is the main source of inspiration for the development of Ant Colony Optimization. In ACO a number of artificial Ants build solutions to an Optimization problem and exchange information on their quality through a communication scheme that is reminiscent of the one adopted by real Ants. ACO algorithms is introduced and all ACO algorithms share the same idea and the ACO is formalized into a meta-heuristics for combinatorial problems. It is foreseeable that future research on ACO will focus more strongly on rich Optimization problems that include stochasticity.

Marco Dorigo – 3rd expert on this subject based on the ideXlab platform

  • Ant Colony Optimization theory: A survey
    Theoretical Computer Science, 2020
    Co-Authors: Marco Dorigo, Christian Blum

    Abstract:

    Research on a new metaheuristic for Optimization is often initially focused on proof-of-concept applications. It is only after experimental work has shown the practical interest of the method that researchers try to deepen their understanding of the method’s functioning not only through more and more sophisticated experiments but also by means of an effort to build a theory. Tackling questions such as “how and why the method works” is importAnt, because finding an answer may help in improving its applicability. Ant Colony Optimization, which was introduced in the early 1990s as a novel technique for solving hard combinatorial Optimization problems, finds itself currently at this point of its life cycle. With this article we provide a survey on theoretical results on Ant Colony Optimization. First, we review some convergence results. Then we discuss relations between Ant Colony Optimization algorithms and other approximate methods for Optimization. Finally, we focus on some research efforts directed at gaining a deeper understanding of the behavior of Ant Colony Optimization algorithms. Throughout the paper we identify some open questions with a certain interest of being solved in the near future. © 2005 Elsevier B.V. All rights reserved.SCOPUS: ar.jinfo:eu-repo/semAntics/publishe

  • Ant Colony Optimization artificial Ants as a computational intelligence technique
    IEEE Computational Intelligence Magazine, 2006
    Co-Authors: Marco Dorigo, Mauro Birattari, Thomas Stutzle

    Abstract:

    The introduction of Ant Colony Optimization (ACO) and to survey its most notable applications are discussed. Ant Colony Optimization takes inspiration from the forging behavior of some Ant species. These Ants deposit Pheromone on the ground in order to mark some favorable path that should be followed by other members of the Colony. The model proposed by Deneubourg and co-workers for explaining the foraging behavior of Ants is the main source of inspiration for the development of Ant Colony Optimization. In ACO a number of artificial Ants build solutions to an Optimization problem and exchange information on their quality through a communication scheme that is reminiscent of the one adopted by real Ants. ACO algorithms is introduced and all ACO algorithms share the same idea and the ACO is formalized into a meta-heuristics for combinatorial problems. It is foreseeable that future research on ACO will focus more strongly on rich Optimization problems that include stochasticity.

  • Ant Colony Optimization theory a survey
    Theoretical Computer Science, 2005
    Co-Authors: Marco Dorigo, Christian Blum

    Abstract:

    Research on a new metaheuristic for Optimization is often initially focused on proof-of-concept applications. It is only after experimental work has shown the practical interest of the method that researchers try to deepen their understanding of the method’s functioning not only through more and more sophisticated experiments but also by means of an effort to build a theory. Tackling questions such as “how and why the method works” is importAnt, because finding an answer may help in improving its applicability. Ant Colony Optimization, which was introduced in the early 1990s as a novel technique for solving hard combinatorial Optimization problems, finds itself currently at this point of its life cycle. With this article we provide a survey on theoretical results on Ant Colony Optimization. First, we review some convergence results. Then we discuss relations between Ant Colony Optimization algorithms and other approximate methods for Optimization. Finally, we focus on some research efforts directed at gaining a deeper understanding of the behavior of Ant Colony Optimization algorithms. Throughout the paper we identify some open questions with a certain interest of being solved in the near future.