Research and improvement on the global convergence of ant colony algorithm

Ant colony algorithm (ACA) is a novel heuristic algorithm, which is based on the process of ants in the nature searching for food. ACA has many good features in optimization, but it has the limitations of stagnation and poor convergence, and is easy to fall in local optima, which are the bottlenecks of its wide application. A detailed theoretical research on the global convergence of ACA is performed. A series of improvement schemes are also proposed. Finally, a typical example of TSP Bayes 29 is calculated. The results verify that the improved ACA has a satisfied global convergence, and lays a good foundation for further research on ACA in theory.