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

Author

Dorigo, Marco ; Gambardella, Luca Maria

Author_Institution

IRIDIA, Vrije Univ., Brussels, Belgium

Volume

1

Issue

1

fYear

1997

fDate

4/1/1997 12:00:00 AM

Firstpage

53

Lastpage

66

Abstract

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

Keywords

cooperative systems; distributed algorithms; learning (artificial intelligence); search problems; travelling salesman problems; ACS-3-opt; ant colony system; cooperating agents; cooperative learning approach; distributed algorithm; evolutionary computation; local search procedure; nature-inspired algorithms; pheromone; simulated annealing; traveling salesman problem; Ant colony optimization; Computational modeling; Distributed algorithms; Evolutionary computation; Feedback; Global communication; Helium; Legged locomotion; Simulated annealing; Traveling salesman problems;

fLanguage

English

Journal_Title

Evolutionary Computation, IEEE Transactions on

Publisher

ieee

ISSN

1089-778X

Type

jour

DOI

10.1109/4235.585892

Filename

585892