An Improved Ant Colony Optimization Algorithm with Crossover Operator

Junen Guo*, Wenguang Diao
Luoyang Institute of Science and Technology, Luoyang, 471023, China

© 2014 Guo and Diao

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: ( This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the College of Mechanical Engineering, Yanshan University, Qinhuangdao, China; Tel: 86-335-8057031; Fax: 86-335-8074783; E-mail:


Ant colony algorithm has been widely applied to lots of fields, such as combinatorial optimization, function optimization, system identification, network routing, robot path planning, data mining and large-scale integrated circuit design of integrated wiring, etc. And it achieved good results. But it still has one weak point which is the slowing convergence speed. To aim at the lacks, an improved ACO is presented. This paper studies a kind of improved ant colony algorithm with crossover operator which makes crossover operator among better results at the end of each iteration. The experiment results indicate that the improved ACO is effectual.

Keywords: Ant colony optimization, combinatorial optimization, convergence speed, crossover operator, genetic algorithm.