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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 metaheuristic
 in New Ideas in Optimization
, 1999
"... Ant algorithms are multiagent 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 multiagent 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
Data Mining with an Ant Colony Optimization Algorithm
 IEEE Transactions on Evolutionary Computation
, 2002
"... Abstract – This work proposes an algorithm for data mining called AntMiner (Ant Colonybased Data Miner). The goal of AntMiner is to extract classification rules from data. The algorithm is inspired by both research on the behavior of real ant colonies and some data mining concepts and principles. ..."
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Cited by 125 (14 self)
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Abstract – This work proposes an algorithm for data mining called AntMiner (Ant Colonybased Data Miner). The goal of AntMiner is to extract classification rules from data. The algorithm is inspired by both research on the behavior of real ant colonies and some data mining concepts and principles. We compare the performance of AntMiner with CN2, a wellknown data mining algorithm for classification, in six public domain data sets. The results provide evidence that: (a) AntMiner is competitive with CN2 with respect to predictive accuracy; and (b) The rule lists discovered by AntMiner are considerably simpler (smaller) than those discovered by CN2. Index Terms – Ant Colony Optimization, data mining, knowledge discovery, classification. I.
Ant Colony Optimization: A New MetaHeuristic
 PROCEEDINGS OF THE CONGRESS ON EVOLUTIONARY COMPUTATION
, 1999
"... Recently, a number of algorithms inspired by the foraging behavior of ant colonies have been applied to the solution of difficult discrete optimization problems. In this paper we put these algorithms in a common framework by defining the Ant Colony Optimization (ACO) metaheuristic. A couple of pa ..."
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Cited by 93 (2 self)
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Recently, a number of algorithms inspired by the foraging behavior of ant colonies have been applied to the solution of difficult discrete optimization problems. In this paper we put these algorithms in a common framework by defining the Ant Colony Optimization (ACO) metaheuristic. A couple of paradigmatic examples of applications of these novel metaheuristic are given, as well as a brief overview of existing applications.
Ant colony optimization for continuous domains
, 2008
"... In this paper we present an extension of ant colony optimization (ACO) to continuous domains. We show how ACO, which was initially developed to be a metaheuristic for combinatorial optimization, can be adapted to continuous optimization without any major conceptual change to its structure. We presen ..."
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Cited by 72 (5 self)
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In this paper we present an extension of ant colony optimization (ACO) to continuous domains. We show how ACO, which was initially developed to be a metaheuristic for combinatorial optimization, can be adapted to continuous optimization without any major conceptual change to its structure. We present the general idea, implementation, and results obtained. We compare the results with those reported in the literature for other continuous optimization methods: other antrelated approaches and other metaheuristics initially developed for combinatorial optimization and later adapted to handle the continuous case. We discuss how our extended ACO compares to those algorithms, and we present some analysis of its efficiency and robustness.
Time Dependent Vehicle Routing Problem with an Ant Colony System
 IDSIA report
, 2003
"... The Time Dependent Vehicle Routing Problem, TDVRP, consists in optimally routing a fleet of vehicles of fixed capacity when the traveling times are dependent on time, that is, the time of the day when the trip on a leg was initiated. The delivery to a customer must also satisfy the customer’s delive ..."
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Cited by 33 (2 self)
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The Time Dependent Vehicle Routing Problem, TDVRP, consists in optimally routing a fleet of vehicles of fixed capacity when the traveling times are dependent on time, that is, the time of the day when the trip on a leg was initiated. The delivery to a customer must also satisfy the customer’s delivery time window. The optimization consists in finding the solution that minimizes the number of tours (the number of vehicles used) and the total travel time. The speed distributions, from which the travel times can be calculated, are supposed to be known at the beginning of the optimization. This version of the VRP is motivated by the fact that in some circumstances, traffic conditions play an important role and can not be ignored in order to perform a realistic optimization. We present a new approach to this problem based on an Ant Colony System (ACS) using time dependent pheromones. Appropriate techniques for local search procedures in a time dependent context are also discussed. We discuss some aspects of this model, such as its tendency to favor travels on longer legs in times when speeds are higher, and how different factors can affect the optimization. From numerical experiments it is showed that best solutions known for the non time dependent case, in a time dependent context, might become unfeasible if time windows are considered, or suboptimal .
A Review on the Ant Colony Optimization Metaheuristic: Basis, Models and New Trends
 Mathware & Soft Computing
, 2002
"... Ant Colony Optimization (ACO) is a recent metaheuristic method that is inspired by the behavior of real ant colonies. In this paper, we review the underlying ideas of this approach that lead from the biological inspiration to the ACO metaheuristic, which gives a set of rules of how to apply ACO ..."
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Cited by 31 (2 self)
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Ant Colony Optimization (ACO) is a recent metaheuristic method that is inspired by the behavior of real ant colonies. In this paper, we review the underlying ideas of this approach that lead from the biological inspiration to the ACO metaheuristic, which gives a set of rules of how to apply ACO algorithms to challenging combinatorial problems. We present some of the algorithms that were developed under this framework, give an overview of current applications, and analyze the relationship between ACO and some of the best known metaheuristics. In addition, we describe recent theoretical developments in the eld and we conclude by showing several new trends and new research directions in this eld.
Extended ant colony optimization for nonconvex mixed integer nonlinear programming
 Comput. Oper. Res
"... Two novel extensions for the well known Ant Colony Optimization (ACO) framework are introduced here, which allow the solution of Mixed Integer Nonlinear Programs (MINLP). Furthermore, a hybrid implementation (ACOmi) based on this extended ACO framework, specially developed for complex nonconvex MI ..."
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Cited by 23 (3 self)
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Two novel extensions for the well known Ant Colony Optimization (ACO) framework are introduced here, which allow the solution of Mixed Integer Nonlinear Programs (MINLP). Furthermore, a hybrid implementation (ACOmi) based on this extended ACO framework, specially developed for complex nonconvex MINLPs, is presented together with numerical results. These extensions on the ACO framework have been developed to serve the needs of practitioners who face highly nonconvex and computationally costly MINLPs. The performance of this new method is evaluated considering several nonconvex MINLP benchmark problems and one realworld application. The results obtained by our implementation substantiate the success of this new approach.
A New Memetic Algorithm for the Asymmetric Traveling Salesman Problem
, 1999
"... This paper introduces a new memetic algorithm particularly designed to be effective with large asymmetric instances of the traveling salesman problem (ATSP). The method incorporates a new local search engine and many other features that contribute to its effectiveness, such as: i) the topological or ..."
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Cited by 21 (6 self)
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This paper introduces a new memetic algorithm particularly designed to be effective with large asymmetric instances of the traveling salesman problem (ATSP). The method incorporates a new local search engine and many other features that contribute to its effectiveness, such as: i) the topological organization of the population of agents as a complete ternary tree with thirteen nodes; ii) the hierarchical organization of the population in overlapping clusters leading to special selection and reproduction schemes; iii) efficient data structures. Computational experiments are conducted on all ATSP instances available in the TSPLIB, and on a set of larger asymmetric instances with known optimal solutions. The comparisons show that the results obtained by our method compare favorably with those obtained by several other algorithms recently proposed for the ATSP.
Using Genetic Algorithms to optimize ACSTSP
 In Ant Algorithms, Third International Workshop, ANTS 2002
, 2002
"... Abstract. We propose the addition of Genetic Algorithms to Ant Colony System (ACS) applied to improve performance. Two modifications are proposed and tested. The first algorithm is a hybrid between ACSTSP and a Genetic Algorithm that encodes experimental variables in ants. The algorithm does not yi ..."
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Cited by 17 (4 self)
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Abstract. We propose the addition of Genetic Algorithms to Ant Colony System (ACS) applied to improve performance. Two modifications are proposed and tested. The first algorithm is a hybrid between ACSTSP and a Genetic Algorithm that encodes experimental variables in ants. The algorithm does not yield improved results but offers concepts that can be used to improve the ACO algorithm. The second algorithm uses a Genetic Algorithm to evolve experimental variable values used in ACSTSP. We have found that the performance of ACSTSP can be improved by using the suggested values. 1