【摘要】 遗传算法具有快速随机的全局搜索能力,但不能很好地利用系统的反馈信息.蚂蚁系统是一种并行的分布式正反馈系统,但初始求解速度慢.遗传算法与蚂蚁算法的融合,优势互补.基于上述思想,提出遗传算法与蚂蚁算法融合的模型与方法,对该方法的收敛性进行了马尔可夫理论分析,并证明其优化解满意值序列是单调不增的和收敛的.且对NP-hard问题中的30城市TSP和中国CHN144城市TSP两个实例进行了实验分析,仿真数据表明该方法不仅是一个逐步收敛的过程,而且求解速度和求解效果都非常好.更多还原

【Abstract】 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.更多还原