A comparative study on the ant colony optimization algorithms

Author

Adubi, Stephen A. ; Misra, Sudip

Author_Institution

Dept. of Comput. & Inf. Sci., Covenant Univ., Ota, Nigeria

fYear

2014

fDate

Sept. 29 2014-Oct. 1 2014

Firstpage

1

Lastpage

4

Abstract

The ant colony optimization (ACO) algorithm is a member of the ant colony algorithms which is part of the swarm intelligence methods. It is a probabilistic technique for finding close to optimal paths through a problem space. The ant colony optimization algorithms therefore mimic the behavior of natural ants with the use of artificial ants as agents to find a reasonable solution to optimization problems by following the model of optimization used by natural ants to get to their destination in the shortest possible time. This paper presents a review and aims to show the main variants of the ant colony optimization algorithms by comparing the results of mainly four variants on some selected combinatorial optimization problems. A review of the varieties of the ACO algorithms, application of ACO algorithms and the comparative analysis of some selected variants are presented.

Keywords

ant colony optimisation; probability; ACO algorithm; ant colony optimization algorithms; artificial ants; combinatorial optimization problems; metaheuristic; natural ants; optimal paths; probabilistic technique; reasonable solution; shortest possible time; swarm intelligence methods; Cities and towns; Color; Optimization; ant colony; metaheuristic; optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronics, Computer and Computation (ICECCO), 2014 11th International Conference on

Conference_Location

Abuja

Print_ISBN

978-1-4799-4108-7

Type

conf

DOI

10.1109/ICECCO.2014.6997567

Filename

6997567