Improvement of ant colony algorithm based on cloud models theory

Ant colony algorithm is a new category of parallelized bionic algorithm in optimization fields. It has strong robustness and is easy to combine with other methods in optimization, but it is easy to fall in local best. Cloud models theory is a powerful tool to convert numerical quantitative analysis to conceptual qualitative analysis. On the basis of introduction of cloud models, a novel qualitative strategy for improving the global optimization properties by use of cloud models is proposed. Finally, the computational experiments on CHC144 TSP have been performed. In the experiments, the rule of increasing half normal cloud is adopted in the improved ant colony algorithm, and the optimal number of cloud drops is 500. Simulation results show that this novel method has certain validity and feasibility.