On the Markov Convergence Analysis for the Combination of Genetic Algorithm and Ant Algorithm
遗传算法与蚂蚁算法融合的马尔可夫收敛性分析

Genetic algorithm has the ability of quickly and stochastically global search-ing, however, it can not make good use of enough output information for systems. Ant system is a parallel-process and distributive-forward system with a relatively slow veloc-ity for providing the solution. Combining genetic and ant algorithms can increase the merits each other. Based on the idea above, the model and method from the combination of genetic and ant algorithms are proposed, and the convergence of the method based on the Markov theory is analysed. Moreover, the conclusion can be drawn that the solution sequence is monotonically decreasing and convergent. The experiment and analysis are carried out for the cases of TSP30 and CHN144 on an NP-hard problem. The results of simulation show that not only the mixed algorithm is a step-by-step convergent process, but also its velocity and effect of solving are quite satisfactory.