This paper introduces the ant colony system (ACS), a distributed algorithm that is applied to the traveling salesman problem (TSP). In the ACS, a set of cooperating agents called ants cooperate to find good solutions to TSPs. Ants cooperate using an indirect form of communication mediated by a pheromone they deposit on the edges of the TSP graph while building solutions. We study the ACS by running experiments to understand its operation. The results show that the ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and we conclude comparing ACS-3-opt, a version of the ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs.

http://www-igm.univ-mlv.fr/~lombardy/ens/JavaTTT0708/fourmis.pdf

Ant colony system: a cooperative learning approach to the traveling salesman problem

Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and of other animals In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful is the general purpose optimization technique known as ant colony optimization Ant colony optimization (ACO) takes inspiration from the foraging behavior of some ant species These ants deposit pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony Ant colony optimization exploits a similar mechanism for solving optimization problems From the early nineties, when the first ant colony optimization algorithm was proposed, ACO attracted the attention of increasing numbers of researchers and many successful applications are now available Moreover, a substantial corpus of theoretical results is becoming available that provides useful guidelines to researchers and practitioners in further applications of ACO The goal of this article is to introduce ant colony optimization and to survey its most notable applications

/pdf/ant-colony-optimization-4lg01ityvg.pdf

Ant Colony Optimization

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.

http://www.iro.umontreal.ca/~dift6751/paper_aco.pdf

Ant algorithms for discrete optimization

Ant Colony Optimization (ACO) is a stochastic local search method that has been inspired by the pheromone trail laying and following behavior of some ant species [1]. Artificial ants in ACO essentially are randomized construction procedures that generate solutions based on (artificial) pheromone trails and heuristic information that are associated to solution components. Since the first ACO algorithm has been proposed in 1991, this algorithmic method has attracted a large number of researchers and in the meantime it has reached a significant level of maturity. In fact, ACO is now a well-established search technique for tackling a wide variety of computationally hard problems.

/pdf/ant-colony-optimization-34xgx5rdns.pdf

The introduction of ant colony optimization (ACO) and to survey its most notable applications are discussed. Ant colony optimization takes inspiration from the forging behavior of some ant species. These ants deposit Pheromone on the ground in order to mark some favorable path that should be followed by other members of the colony. The model proposed by Deneubourg and co-workers for explaining the foraging behavior of ants is the main source of inspiration for the development of ant colony optimization. In ACO a number of artificial ants build solutions to an optimization problem and exchange information on their quality through a communication scheme that is reminiscent of the one adopted by real ants. ACO algorithms is introduced and all ACO algorithms share the same idea and the ACO is formalized into a meta-heuristics for combinatorial problems. It is foreseeable that future research on ACO will focus more strongly on rich optimization problems that include stochasticity.

/pdf/ant-colony-optimization-artificial-ants-as-a-computational-4d2lxwbp0x.pdf

Ant colony optimization: artificial ants as a computational intelligence technique

This article describes a novel method of achieving load balancing in telecommunications networks. A simulated network models a typical distribution of calls between nodes; nodes carrying an excess ...

Ant-based load balancing in telecommunications networks

This paper describes a novel method of achieving load balancing in telecommunications networks. A simulated network models a typical distribution of calls between arbitrary nodes; nodes carrying an excess of traffic can become congested , causing calls to fail. In addition to calls, the network also supports a population of simple mobile agents with behaviours modelled on the trail laying abilities of ants. The agents move across the network between arbitrary pairs of nodes, selecting their path at each intermediate node according to the distribution of simulated pheromones at each node. As they move they deposit simulated pheromones as a function of their distance from their source node, and the congestion encountered on their journey. Calls between nodes are routed as a function of the pheromone distributions at each intermediate node. The performance of the network is measured by the proportion of calls which fail. The results are compared with those achieved by using fixed shortest-path routes, and also by using an alternative algorithmically-based type of mobile agent. The ant-based system is shown to drop fewer calls than the other methods, while exhibiting many attractive features of distributed control.

Ant-like agents for load balancing in telecommunications networks

This chapter describes an alternative way of controlling dynamic routing in a communications network. The technique used is based on simple mobile software agents modeled on ants which continually modify the routing tables in response to congestion in the network, and which co-ordinate their actions using stigmergy, a form of indirect communication used by social insects. The principle is demonstrated on a simulated communications network modeling typical distributions of calls between nodes. The network also supports a population of mobile software agents — the ants —designed to imitate the behavior of certain ants, which lay and follow trails of scent, or pheromone. As the ants move across the network between randomly chosen pairs of nodes, they deposit simulated pheromone at each node as a function of the distance traveled and the congestion encountered on the journey. They select their path at each intermediate node as a function of the distribution of simulated pheromone at that node. Calls are also routed according to the pheromone distributions at each intermediate node. The ant-based control is compared with other routing methods on the ability to deal with changing call distributions and high network loads. Despite their simplicity the ant-like agents are shown to adapt smoothly to the ever-changing and complex behavior of the network, preventing or removing congestion by distributing the load on the network evenly and keeping the average load low. The system is shown to exhibit many attractive features deriving from the same factors that make natural ants successful. The prospects for extending this type of distributed control to the Internet are also considered.

Ruud Schoonderwoerd

Papers

Ant-based load balancing in telecommunications networks

Ant-like agents for load balancing in telecommunications networks

Minimal Agents for Communications Network Routing: The Social Insect Paradigm