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138
Ant algorithms for discrete optimization
- ARTIFICIAL LIFE
, 1999
"... This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies’ foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic ..."
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Cited by 489 (42 self)
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This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies’ foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic.
The ant colony optimization meta-heuristic
- in New Ideas in Optimization
, 1999
"... Ant algorithms are multi-agent systems in which the behavior of each single agent, called artificial ant or ant for short in the following, is inspired by the behavior of real ants. Ant algorithms are one of the most successful examples of swarm intelligent systems [3], and have been applied to many ..."
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Cited by 389 (23 self)
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Ant algorithms are multi-agent systems in which the behavior of each single agent, called artificial ant or ant for short in the following, is inspired by the behavior of real ants. Ant algorithms are one of the most successful examples of swarm intelligent systems [3], and have been applied to many types of problems, ranging from the classical traveling salesman
A Racing Algorithm for Configuring Metaheuristics
, 2002
"... This paper describes a racing procedure for finding, in a limited amount of time, a configuration of a metaheuristic that performs as good as possible on a given instance class of a combinatorial optimization problem. Taking inspiration from methods proposed in the machine learning literature ..."
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Cited by 164 (34 self)
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This paper describes a racing procedure for finding, in a limited amount of time, a configuration of a metaheuristic that performs as good as possible on a given instance class of a combinatorial optimization problem. Taking inspiration from methods proposed in the machine learning literature for model selection through cross-validation, we propose a procedure that empirically evaluates a set of candidate configurations by discarding bad ones as soon as statistically sufficient evidence is gathered against them. We empirically evaluate our procedure using as an example the configuration of an ant colony optimization algorithm applied to the traveling salesman problem.
MAX-MIN Ant System
, 1999
"... Ant System, the first Ant Colony Optimization algorithm, showed to be a viable method for attacking hard combinatorial optimization problems. Yet, its performance, when compared to more fine-tuned algorithms, was rather poor for large instances of traditional benchmark problems like the Traveling Sa ..."
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Cited by 128 (3 self)
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Ant System, the first Ant Colony Optimization algorithm, showed to be a viable method for attacking hard combinatorial optimization problems. Yet, its performance, when compared to more fine-tuned algorithms, was rather poor for large instances of traditional benchmark problems like the Traveling Salesman Problem. To show that Ant Colony Optimization algorithms could be good alternatives to existing algorithms for hard combinatorial optimization problems, recent research in this ares has mainly focused on the development of algorithmic variants which achieve better performance than AS. In this article, we present¨�©� � –¨��� � Ant System, an Ant Colony Optimization algorithm derived from Ant System.¨�©� � –¨��� � Ant System differs from Ant System in several important aspects, whose usefulness we demonstrate by means of an experimental study. Additionally, we relate one of the characteristics specific to¨� ¨ AS — that of using a greedier search than Ant System — to results from the search space analysis of the combinatorial optimization problems attacked in this paper. Our computational results on the Traveling Salesman Problem and the Quadratic Assignment Problem show that ¨�©� � –¨��� � Ant System is currently among the best performing algorithms for these problems.
An Improved Ant System Algorithm for the Vehicle Routing Problem
- Annals of Operations Research
, 1997
"... this paper an improved ant system algorithm for the Vehicle Routing Problem with one central depot and identical vehicles. Computational results on fourteen benchmark problems from the literature are reported and a comparison with five other metaheuristic approaches to solve vehicle routing problems ..."
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Cited by 125 (10 self)
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this paper an improved ant system algorithm for the Vehicle Routing Problem with one central depot and identical vehicles. Computational results on fourteen benchmark problems from the literature are reported and a comparison with five other metaheuristic approaches to solve vehicle routing problems is made.
The ant colony optimization metaheuristic: Algorithms, applications, and advances
- Handbook of Metaheuristics
, 2002
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ACO Algorithms for the Traveling Salesman Problem
- Periaux (eds), Evolutionary Algorithms in Engineering and Computer Science: Recent Advances in Genetic Algorithms, Evolution Strategies, Evolutionary Programming, Genetic Programming and Industrial Applications
, 1999
"... Ant algorithms [18, 14, 19] are a recently developed, population-based approach which has been successfully applied to several NP-hard combinatorial ..."
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Cited by 66 (7 self)
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Ant algorithms [18, 14, 19] are a recently developed, population-based approach which has been successfully applied to several NP-hard combinatorial
Ant Colony Optimization -- Artificial Ants as a Computational Intelligence Technique
- IEEE COMPUT. INTELL. MAG
, 2006
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Model-based search for combinatorial optimization
, 2001
"... Abstract In this paper we introduce model-based search as a unifying framework accommodating some recently proposed heuristics for combinatorial optimization such as ant colony optimization, stochastic gradient ascent, cross-entropy and estimation of distribution methods. We discuss similarities as ..."
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Cited by 64 (12 self)
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Abstract In this paper we introduce model-based search as a unifying framework accommodating some recently proposed heuristics for combinatorial optimization such as ant colony optimization, stochastic gradient ascent, cross-entropy and estimation of distribution methods. We discuss similarities as well as distinctive features of each method, propose some extensions and present a comparative experimental study of these algorithms. 1
An Ant Approach to the Flow Shop Problem
- IN PROCEEDINGS OF THE 6TH EUROPEAN CONGRESS ON INTELLIGENT TECHNIQUES & SOFT COMPUTING (EUFIT'98
, 1997
"... In this article we present an ant based approach to Flow Shop Scheduling problems. Ant Colony Optimization is a new algorithmic approach, inspired by the behavior of real ants, that can be used for the solution of combinatorial optimization problems. (Artificial) ants are used to construct solutions ..."
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Cited by 61 (9 self)
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In this article we present an ant based approach to Flow Shop Scheduling problems. Ant Colony Optimization is a new algorithmic approach, inspired by the behavior of real ants, that can be used for the solution of combinatorial optimization problems. (Artificial) ants are used to construct solutions for Flow Shop Problems that subsequently are improved by a local search procedure. We compare the results obtained with our procedure to some basic heuristics for Flow Shop Problems, showing that our approach is very promising for the FSP.