MAX-MIN Ant System and Local Search for Combinatorial Optimization Problems (1997)
| Citations: | 29 - 6 self |
BibTeX
@MISC{Stützle97max-minant,
author = {Thomas Stützle and Holger Hoos},
title = {MAX-MIN Ant System and Local Search for Combinatorial Optimization Problems},
year = {1997}
}
Years of Citing Articles
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
Abstract
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...
