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22
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 crossvalidation, 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.
MAXMIN Ant System and Local Search for the Traveling Salesman Problem
 IEEE INTERNATIONAL CONFERENCE ON EVOLUTIONARY COMPUTATION (ICEC'97)
, 1997
"... Ant System is a general purpose algorithm inspired by the study of the behavior of Ant Colonies. It is based on a cooperative search paradigm that is applicable to the solution of combinatorial optimization problems. In this paper we introduce MAX MIN Ant System, an improved version of basic Ant S ..."
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Cited by 138 (15 self)
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Ant System is a general purpose algorithm inspired by the study of the behavior of Ant Colonies. It is based on a cooperative search paradigm that is applicable to the solution of combinatorial optimization problems. In this paper we introduce MAX MIN Ant System, an improved version of basic Ant System, and report our results for its application to symmetric and asymmetric instances of the well known Traveling Salesman Problem. We show how MAX MIN Ant System can be significantly improved extending it with local search heuristics. Our results clearly show that MAX MIN Ant System has the property of effectively guiding the local search heuristics towards promising regions of the search space by generating good initial tours. I. Introduction The Ant System algorithm, originally introduced in [3], [4], is a new cooperative search algorithm inspired by the behavior of real ants. Ants are able to find good solutions to shortest path problems between a food source and their home colony...
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.
Editorial survey: swarm intelligence for data mining
 MACH LEARN (2011) 82: 1–42
, 2011
"... This paper surveys the intersection of two fascinating and increasingly popular domains: swarm intelligence and data mining. Whereas data mining has been a popular academic topic for decades, swarm intelligence is a relatively new subfield of artificial intelligence which studies the emergent col ..."
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Cited by 26 (0 self)
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This paper surveys the intersection of two fascinating and increasingly popular domains: swarm intelligence and data mining. Whereas data mining has been a popular academic topic for decades, swarm intelligence is a relatively new subfield of artificial intelligence which studies the emergent collective intelligence of groups of simple agents. It is based on social behavior that can be observed in nature, such as ant colonies, flocks of birds, fish schools and bee hives, where a number of individuals with limited capabilities are able to come to intelligent solutions for complex problems. In recent years the swarm intelligence paradigm has received widespread attention in research, mainly as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). These are also the most popular swarm intelligence metaheuristics for data mining. In addition to an overview of these nature inspired computing methodologies, we discuss popular data mining techniques based on these principles and schematically list the main differences in our literature tables. Further, we provide a unifying framework that categorizes the swarm intelligence based data mining algorithms into two approaches: effective search and data organizing. Finally, we list interesting issues for future research, hereby identifying methodological gaps in current research as well as mapping opportunities provided by swarm intelligence to current challenges within data mining research.
A hybrid evolutionary algorithm for traveling salesman problem
 in Proceedings of Congress on Evolutionary Computation, 2004, CEC2004
, 2004
"... This paper details the development of a Hybrid Evolutionary Algorithm for solving the Traveling Salesman Problem (TSP). The strategy of the algorithm is to complement and extend the successful results of a genetic algorithm (GA) using a distance preserving crossover (DPX) by incorporating memory in ..."
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Cited by 10 (0 self)
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This paper details the development of a Hybrid Evolutionary Algorithm for solving the Traveling Salesman Problem (TSP). The strategy of the algorithm is to complement and extend the successful results of a genetic algorithm (GA) using a distance preserving crossover (DPX) by incorporating memory in the form of ant pheromone during the city selection process. The synergistic combination of the DPXGA with city selection based on probability determined by both distance and previous success incorporates additional information into the search mechanism. This combination into a Hybrid GA facilitates finding quality solutions for TSP problems with lower computation complexity. This study represents a preliminary investigation with direct comparison to show the feasibility and promise of this hybrid approach. 1.
The Influence of RunTime Limits on Choosing Ant System Parameters
 In Proc. Genetic and Evolutionary Computation Conference (GECCO 2003
, 2003
"... The influence of the allowed running time on the choice of the parameters of an ant system is investigated. It is shown that different parameter values appear to be optimal depending on the algorithm runtime. The performance of the MAXMIN Ant System (MMAS) on the University Course Timetabling Prob ..."
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Cited by 7 (2 self)
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The influence of the allowed running time on the choice of the parameters of an ant system is investigated. It is shown that different parameter values appear to be optimal depending on the algorithm runtime. The performance of the MAXMIN Ant System (MMAS) on the University Course Timetabling Problem (UCTP)  a type of constraint satisfaction problem  is used as an example. The parameters taken into consideration include the type of the local search used, and some typical parameters for MMAS  the tau_min and rho. It is shown that the optimal parameters depend significantly on the time limits set. Conclusions summarizing the influence of time limits on parameter choice, and possible methods of making the parameter choice more independent from the time limits, are presented.
Experiments with Variants of Ant Algorithms
 Mathware & Soft Computing, this issue
, 2000
"... A number of extensions of Ant System, the first ant colony optimization (ACO) algorithm, were proposed in the literature. These extensions typically achieve much improved computational results when compared to the original Ant System. However, many design choices of Ant System are left untouched inc ..."
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Cited by 3 (1 self)
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A number of extensions of Ant System, the first ant colony optimization (ACO) algorithm, were proposed in the literature. These extensions typically achieve much improved computational results when compared to the original Ant System. However, many design choices of Ant System are left untouched including the fact that solutions are constructed, that realnumbers are used to simulate pheromone trails, and that explicit pheromone evaporation is used. In this article we experimentally investigate adaptations of ant algorithms to the traveling salesman problem that use alternative choices for these latter features: we consider using pheromones to modify solutions and different schemes for manipulating pheromone trails based on integer pheromone trails without recurring to pheromone evaporation.
A New Ant Algorithm for Graph Coloring
"... Abstract. Let G = (V, E) be a graph with vertex set V and edge set E. The kcoloring problem is to assign a color (a number chosen in {1,..., k}) to each vertex of V so that no edge has both endpoints with the same color. We describe in this paper a new ant algorithm for the kcoloring problem. Comp ..."
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Cited by 2 (0 self)
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Abstract. Let G = (V, E) be a graph with vertex set V and edge set E. The kcoloring problem is to assign a color (a number chosen in {1,..., k}) to each vertex of V so that no edge has both endpoints with the same color. We describe in this paper a new ant algorithm for the kcoloring problem. Computational experiments give evidence that our algorithm is competitive with the existing ant algorithms for this problem, while giving a minor role to each ant. Our algorithm is however not competitive with the best known coloring algorithms 1 Introduction to graph coloring The graph coloring problem (GCP for short) can be described as follows. Given a graph G = (V, E) with vertex set V and edge set E, and given an integer k, a kcoloring of G is a function col: V − → {1,..., k}. The value col(x) of a vertex x is called the color of x. Vertices with a same color define a color class. If two adjacent vertices x and y have the
A MAXMIN Ant Colony System for Minimum Common String Partition Problem
, 2014
"... In this paper, we consider the problem of finding a minimum common partition of two strings (MCSP). The problem has its application in genome comparison. As it is an NPhard, discrete combinatorial optimization problem, we employ a metaheuristic technique, namely, MAXMIN ant system to solve this. T ..."
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Cited by 1 (1 self)
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In this paper, we consider the problem of finding a minimum common partition of two strings (MCSP). The problem has its application in genome comparison. As it is an NPhard, discrete combinatorial optimization problem, we employ a metaheuristic technique, namely, MAXMIN ant system to solve this. The experimental results are found to be promising.