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A review on the ant colony optimization metaheuristic: basis, models and new trends.

Oscar CordónFrancisco HerreraThomas Stützle — 2002

Mathware and Soft Computing

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...

Ant Colony Optimisation: models and applications.

Oscar CordónFrancisco HerreraThomas Stützle — 2002

Mathware and Soft Computing

Ant Colony Optimization (ACO) is a metaheuristic that is inspired by the shortest path searching behavior of various ant species [1,2]. The initial work of Dorigo, Maniezzo and Colorni [3,4] who proposed the first ACO algorithm called Ant System, has stimulated a still strongly increasing number of researchers to develop more sophisticated and better performing ACO algorithms that are used to successfully solve a large number of hard combinatorial optimization problems such as the traveling salesman...

A GA-P algorithm to automatically formulate extended Boolean queries for a fuzzy information retrieval system.

Oscar CordónFélix de MoyaCarmen Zarco — 2000

Mathware and Soft Computing

Although the fuzzy retrieval model constitutes a powerful extension of the Boolean one, being able to deal with the imprecision and subjectivity existing in the Information Retrieval process, users are not usually able to express their query requirements in the form of an extended Boolean query including weights. To solve this problem, different tools to assist the user in the query formulation have been proposed. In this paper, the genetic algorithm-programming technique is considered to build...

Analysis of the best-worst ant system and its variants on the TSP.

Oscar CordónIñaki Fernández de VianaFrancisco Herrera — 2002

Mathware and Soft Computing

In this contribution, we will study the influence of the three main components of Best-Worst Ant System: the best-worst pheromone trail update rule, the pheromone trail mutation and the restart. Both the importance of each of them and the fact whether all of them are necessary will be analyzed. The performance of different variants of this algorithm will be tested when solving different instances of the TSP.

Analyzing the reasoning mechanisms in fuzzy rule based classification systems.

Oscar CordónMaría José del JesúsFrancisco Herrera — 1998

Mathware and Soft Computing

Fuzzy Rule-Based Systems have been succesfully applied to pattern classification problems. In this type of classification systems, the classical Fuzzy Reasoning Method classifies a new example with the consequent of the rule with the greatest degree of association. By using this reasoning method, we do not consider the information provided by the other rules that are also compatible (have also been fired) with this example. In this paper we analyze this problem and propose to use FRMs...

Improvement to the cooperative rules methodology by using the ant colony system algorithm.

Rafael AlcaláJorge CasillasOscar CordónFrancisco Herrera — 2001

Mathware and Soft Computing

The cooperative rules (COR) methodology [2] is based on a combinatorial search of cooperative rules performed over a set of previously generated candidate rule consequents. It obtains accurate models preserving the highest interpretability of the linguistic fuzzy rule-based systems. Once the good behavior of the COR methodology has been proven in previous works, this contribution focuses on developing the process with a novel kind of metaheuristic algorithm: the ant colony system one. Thanks to...

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