Ant colony optimization with different crossover schemes for global optimization

Global optimization, especially large scale optimization problems arise as a very interesting field of research, because they appear in many real-world problems. Ant colony optimization is one of optimization techniques for these problems. In this paper, we improve the continuous ant colony optimization (ACO with crossover operator. Three crossover methods are employed to generate some new probability density function set of ACO. The proposed algorithms are evaluated by using 21 benchmark functions whose dimensionality is 30-1000. The simulation results show that the proposed ACO with different crossover operators significantly enhance the performance of ACO for global optimization. In the case the dimensionality is 1000, the proposed algorithm also can efficiently solves them. Compared with state-of-art algorithms, the proposal is a very competitive optimization algorithm for global optimization problems.