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 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.