Hybridizing ant colony optimization with firefly algorithm for unconstrained optimization problems

we propose a novel hybrid algorithm named ACO-FA, which integrates the merits of ant colony optimization (ACO) with firefly algorithm (FA) to solve unconstrained optimization problems. The main feature of the hybrid algorithm is to hybridize the solution construction mechanism of the ACO with the FA. In our hybrid algorithm, the initial solutions are generated randomly from the search space, and then we employ ACO algorithm to generate new solutions through updating the common arc pheromone mechanism. Moreover, during implementing the hybrid algorithm, a new class of swarm intelligence algorithm can also be embedded into the FA framework as neighborhood search to improve solutions. 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. Keywords-ant colony optimization; firefly algorithm; swarm intelligence algorithm; unconstrained optimization.