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Christian Blum - One of the best experts on this subject based on the ideXlab platform.
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Ant Colony Optimization theory: A survey
Theoretical Computer Science, 2020Co-Authors: Marco Dorigo, Christian BlumAbstract: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
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HIS - Ant Colony Optimization: Introduction and Hybridizations
7th International Conference on Hybrid Intelligent Systems (HIS 2007), 2007Co-Authors: Christian BlumAbstract: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.
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Ant Colony Optimization: Introduction and hybridizations
Proceedings - 7th International Conference on Hybrid Intelligent Systems HIS 2007, 2007Co-Authors: Christian BlumAbstract: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.
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Ant Colony Optimization introduction and recent trends
Physics of Life Reviews, 2005Co-Authors: Christian BlumAbstract: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.
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Ant Colony Optimization theory a survey
Theoretical Computer Science, 2005Co-Authors: Marco Dorigo, Christian BlumAbstract: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.
Thomas Stutzle - One of the best experts on this subject based on the ideXlab platform.
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A critical analysis of parameter adaptation in Ant Colony Optimization
Swarm Intelligence, 2011Co-Authors: Paola Pellegrini, Thomas Stutzle, Mauro BirattariAbstract: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.
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Ant Colony Optimization
International Conference on Evolutionary Multi-criterion Optimization, 2009Co-Authors: Thomas StutzleAbstract: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.
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Ant Colony Optimization artificial Ants as a computational intelligence technique
IEEE Computational Intelligence Magazine, 2006Co-Authors: Marco Dorigo, Mauro Birattari, Thomas StutzleAbstract: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.
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Ant Colony Optimization
2004Co-Authors: Marco Dorigo, Mauro Birattari, Thomas StutzleAbstract:Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals. In particular, Ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose Optimization technique known as Ant Colony Optimization. Ant Colony Optimization (ACO) takes inspiration from the foraging 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. Ant Colony Optimization exploits a similar mechanism for solving Optimization problems. From the early nineties, when the first Ant Colony Optimization algorithm was proposed, ACO attracted the attention of increasing numbers of researchers and many successful applications are now available. Moreover, a substAntial corpus of theoretical results is becoming available that provides useful guidelines to researchers and practitioners in further applications of ACO. The goal of this article is to introduce Ant Colony Optimization and to survey its most notable applications
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parallelization strategies for Ant Colony Optimization
Parallel Problem Solving from Nature, 1998Co-Authors: Thomas StutzleAbstract:Ant Colony Optimization (ACO) is a new population oriented search metaphor that has been successfully applied to NP-hard combinatorial Optimization problems. In this paper we discuss parallelization strategies for Ant Colony Optimization algorithms. We empirically test the most simple strategy, that of executing parallel independent runs of an algorithm. The empirical tests are performed applying MAX-MIN Ant System, one of the most efficient ACO algorithms, to the Traveling Salesman Problem and show that using parallel independent runs is very effective.
Marco Dorigo - One of the best experts on this subject based on the ideXlab platform.
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Ant Colony Optimization theory: A survey
Theoretical Computer Science, 2020Co-Authors: Marco Dorigo, Christian BlumAbstract: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
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Ant Colony Optimization artificial Ants as a computational intelligence technique
IEEE Computational Intelligence Magazine, 2006Co-Authors: Marco Dorigo, Mauro Birattari, Thomas StutzleAbstract: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.
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Ant Colony Optimization theory a survey
Theoretical Computer Science, 2005Co-Authors: Marco Dorigo, Christian BlumAbstract: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.
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Ant Colony Optimization theory: A survey
Theoretical Computer Science, 2005Co-Authors: Marco Dorigo, Christian BlumAbstract: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.
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AntS Workshop - Deception in Ant Colony Optimization
Ant Colony Optimization and Swarm Intelligence, 2004Co-Authors: Christian Blum, Marco DorigoAbstract:The search process of a metaheuristic is sometimes misled. This may be caused by features of the tackled problem instance, by features of the algorithm, or by the chosen solution representation. In the field of evolutionary computation, the first case is called deception and the second case is referred to as bias. In this work we formalize the notions of deception and bias for Ant Colony Optimization. We formally define first order deception in Ant Colony Optimization, which corresponds to deception as being described in evolutionary computation. Furthermore, we formally define second order deception in Ant Colony Optimization, which corresponds to the bias introduced by components of the algorithm in evolutionary computation. We show by means of an example that second order deception is a potential problem in Ant Colony Optimization algorithms.
Meipeng Zhong - One of the best experts on this subject based on the ideXlab platform.
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Parameter Optimization of Compressor Based on an Ant Colony Optimization
Applied Mechanics and Materials, 2012Co-Authors: Meipeng ZhongAbstract:A mathematical model of operation on air compressors is set up in order to improve the efficiency of air compressors. Parameter of Compressor is optimized by an Ant Colony Optimization (ACO) Particle approach. Volume and its weight of the new compressor are little, and its efficiency is high. An Ant Colony Optimization embed BLDCM module which optimizating the air compressor was put forward. Optimizated target of an Ant Colony Optimization is the efficiency of BLDCM. Optimizated variables are the diameter of low pressure cylinder, the diameter of high pressure cylinder, the journey of low pressure piston, the journey of high pressure piston and the rotate speed of BLDCM. Simulated result shows that the efficiency of BLDCM is more than that before optimizating. The test is done. The result shows that the specifc Power of air compressor is much less than before optimizating on 2.5Mpa. The result also shows that an Ant Colony Optimization which optimizating the air compressor is availability and practicality.
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Parameter Optimization of Compressor Based on an Ant Colony Optimization
Applied Mechanics and Materials, 2012Co-Authors: Meipeng ZhongAbstract:A mathematical model of operation on air compressors is set up in order to improve the efficiency of air compressors. Parameter of Compressor is optimized by an Ant Colony Optimization (ACO) Particle approach. Volume and its weight of the new compressor are little, and its efficiency is high. An Ant Colony Optimization embed BLDCM module which optimizating the air compressor was put forward. Optimizated target of an Ant Colony Optimization is the efficiency of BLDCM. Optimizated variables are the diameter of low pressure cylinder, the diameter of high pressure cylinder, the journey of low pressure piston, the journey of high pressure piston and the rotate speed of BLDCM. Simulated result shows that the efficiency of BLDCM is more than that before optimizating. The test is done. The result shows that the specifc Power of air compressor is much less than before optimizating on 2.5Mpa. The result also shows that an Ant Colony Optimization which optimizating the air compressor is availability and practicality. © (2012) Trans Tech Publications, Switzerland.
L.r. Srinivas - One of the best experts on this subject based on the ideXlab platform.
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Evolving Ant Colony Optimization based unit commitment
Applied Soft Computing, 2011Co-Authors: K. Vaisakh, L.r. SrinivasAbstract:Ant Colony Optimization (ACO) was inspired by the observation of natural behavior of real Ants' pheromone trail formation and foraging. Ant Colony Optimization is more suitable for combinatorial Optimization problems. ACO is successfully applied to the traveling salesman problem. Multistage decision making of ACO gives an edge over other conventional methods. This paper proposes evolving Ant Colony Optimization (EACO) method for solving unit commitment (UC) problem. The EACO employs genetic algorithm (GA) for finding optimal set of ACO parameters, while ACO solves the UC problem. Problem formulation takes into consideration the minimum up and down time constraints, startup cost, spinning reserve, and generation limit constraints. The feasibility of the proposed approach is demonstrated on two different systems. The test results are encouraging and compared with those obtained by other methods.
