优化蚁群算法在网络知识路由系统中的应用

研究网络知识路由问题，提高网络资源搜索质量。针对传统方法在网络资源搜索过程中，存在搜索时间长，得不到最优解，导致搜索速度慢，效率低的问题。为了提高网络资源搜索效率，提出一种基于改进蚁群的路径搜索算法，在混合信息素更新策略，自适应挥发因子等方面进行改进，并设置了先行蚂蚁和后行蚂蚁。该方法有效地避免了蚁群搜索陷入局部最优，加快了收敛，提高了搜索效率。仿真结果表明，改进方法缩短了搜索时间，网络资源搜索效率明显提高，证明是一种有效的优化方法，能够在最短时间找到资源搜索的最优解，是解决网络资源搜索优化问题的有效算法。

Application of Ant Colony Optimization algorithm to knowledge routing system

Knowledge routing system are studied to improve the quality of network resources. In the course of searching network resources, the traditional method takes a long time and could not get the global optimal solution, resulting in slow search speed and low efficiency. In order to improve searching efficiency of network resources, the paper puts forward a path search algorithm based on improved Ant Colony Optimization, focusing on improving hybrid pheromone update strategy, adaptive volatile factor, etc, meanwhile setting the first ants and after ants. The improved ant colony could effectively avoid falling into local optimum, speed up the convergence and increase the searching efficiency. Simulation results show that the improved method is an effective optimization method, which could shorten the searching time and improve the searching efficiency of network resources, and find the optimal solution in the shortest time. Therefore it could be proved that this algorithm is an optimization solution in the problem of network resources.