Hybridizing ant colony optimization with firefly algorithm for unconstrained optimization problems

We propose a novel hybrid algorithm named ACO–FA, which integrate ant colony optimization (ACO) with firefly algorithm (FA) to solve unconstrained optimization problems. The proposed algorithm integrates the merits of both ACO and FA and it has two characteristic features. Firstly, the algorithm is...

Full description

Saved in:
Bibliographic Details
Published in:Applied mathematics and computation 2013-11, Vol.224, p.473-483
Main Authors: Rizk-Allah, R.M., Zaki, Elsayed M., El-Sawy, Ahmed Ahmed
Format: Article
Language:English
Subjects:
Algorithms
Ant colony optimization
Evolution
Fireflies
Firefly algorithm
Mathematical models
Optimization
Randomization
Searching
Unconstrained optimization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We propose a novel hybrid algorithm named ACO–FA, which integrate ant colony optimization (ACO) with firefly algorithm (FA) to solve unconstrained optimization problems. The proposed algorithm integrates the merits of both ACO and FA and it has two characteristic features. Firstly, the algorithm is initialized by a population of random ants that roam through the search space. During this roaming an evolution of these ants are performed by integrating ACO and FA, where FA works as a local search to refine the positions found by the ants. Secondly, the performance of FA is improved by reducing the randomization parameter so that it decreases gradually as the optima are approaching. Finally, the proposed algorithm ACO–FA is tested on several benchmark problems from the usual literature and the numerical results have demonstrated the superiority of the proposed algorithm for finding the global optimal solution.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2013.07.092