基于混沌蚁群算法的最短路径选择研究

如何解决最短路径选择问题一直是城市交通流诱导系统的关键之一.基于群体仿生理论的蚁群算法是解决此问题的一种方法,针对采用蚁群算法进行最短路径选择时易出现的陷入局部最优解问题,引入混沌理论,采用混沌蚁群算法利用混沌初始化进行改善个体质量和利用混沌扰动避免在蚁群算法搜索过程中陷入局部极值,同时降低了蚁群算法的时间复杂度,从而更好的解决了最短路径选择问题.

Study on Shortest Path Search Method Based on Chaos Ant Colony Optimization

Searching shortest path is one of the most important issues of Traffic Route Guidance System.Chaos Ant Colony Optimization is a kind of population based on bionic algorithm,which is one of the methods for the problem.Because the ant colony algorithm is easy to drop into local optima as searching the shortest path,a chaotic search algorithm is embedded into the modified venison of special ant colony optimization algorithm which is called Chaos Ant Colony Optimization(CACO).The basic principle of CPSO algorithm is that chaos initialization should be adopted to improve individual quality and chaos perturbation should be utilized to avoid the search being trapped in local optimum.It makes the time complexity of the ant ACO going down,and is a good solution to the problem of searching shortest path.