Although genetic algorithm (GA) has the ability to do quick and stochastic global search, it can’t efficiently use the output information for systems. Ant algorithm (AA), on the other hand, is a parallel-proceed and distributive-forward system with a relatively slow speed for carrying out its solution. Incorporating GA and AA can improve their merits one for another. In this paper, the model and the method from the combination of GA and AA are proposed. The convergence of the method based on the Markov theory is analyzed. The experiment and analysis are conducted on the NP-hard problems for the cases of TSP30 (Travel Salesman Problem 30 cities) and CHN144 (China 144 cities). This work proves that the satisfactory solution sequence is monotonically decreasing and convergent. The results of simulations show that not only this combined algorithm is a step-by-step convergent process, but also its speed and effect of solving are quite satisfactory.
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- Model and Convergence for the Combination of Genetic Algorithm and Ant Algorithm
- Springer Berlin Heidelberg