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.

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

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.

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.

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.

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. 1 Introduction The Flow Shop Problem (FSP) can be stated as follows: Each of n jobs 1; : : : ; n have to be processed on m machines 1; : : : ; m in that order. The processing time of job i on machine j is t ij . The processing times are fixed, nonnegative, and may be 0 if a job is not processed on some machine. Further assumptions are that each job can be processed on only one machine at a time, the operations are not preemptable, the jobs are avai...

Ant algorithms [18, 14, 19] are a recently developed, population-based approach which has been successfully applied to several NP-hard combinatorial

. A simple mechanism is presented, based on ant-like agents, for routing and load balancing in telecommunications networks, following the initial works of Appleby and Stewart (1994) and Schoonderwoerd et al. (1997). In the present work, agents are very similar to those proposed by Schoonderwoerd et al. (1997), but a r e supplemented with a simplified dynamic programming capability, initially experimented by Gurin (1997) with more complex agents, which is shown to significantly improve the network's relaxation and its response to perturbations. Topic area: Intelligent agents and network management 2 1. Introduction 1.1 Routing in telecommunications networks Routing is a mechanism that allows calls to be transmitted from a source to a destination through a sequence of intermediate switching stations or nodes, because not all points are directly connected: the cost of completely connecting a network becomes prohibitive for more than a few nodes. Routing selects routes that meet the o...

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

In this paper we present an extension of MAX --MIN Ant System applying it to Traveling Salesman Problems and Quadratic Assignment Problems. The extension involves the use of a modified choice rule and a hybrid scheme allowing ants to improve their solution by local search. The computational results show that this algorithm can be used to efficiently find near optimal solutions to hard combinatorial optimization problems and is one of the best methods for the solution of structured quadratic assignment problems. 1 Introduction Ant Colony Optimization (ACO) is a population based, cooperative search metaphor inspired by the foraging behavior of real ants. One of the basic ideas of ACO is to use the equivalent of the pheromone trail used by real ants as a medium for cooperation and communication among a colony of artificial ants. The seminal work on ACO is Ant System [8, 10] that was first proposed for solving the Traveling Salesman Problem (TSP). In Ant System, the ants are simple agent...