| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 384518 | Expert Systems with Applications | 2009 | 6 Pages |
Abstract
The ant colony optimization (ACO) algorithms, which are inspired by the behaviour of ants to find solutions to combinatorial optimization problem, are multi-agent systems. This paper presents the ACO-based algorithm that is used to find the global minimum of a nonconvex function. The algorithm is based on that each ant searches only around the best solution of the previous iteration. This algorithm was tested on some standard test functions, and successful results were obtained. Its performance was compared with the other algorithms, and was observed to be better.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
M. Duran Toksarı,