基于香味素诱导及道路分级的OPP问题的蚁群算法

Ant Colony Optimization Algorithm Based on Scent Inducement and Route Classification for the Optimal Path Problems

为解决传统蚁群算法在求解最优路径问题(optimal path problems,OPP)时,搜索效率不高、最优解质量偏低的问题,提出了一种基于香味素诱导和道路分级的蚁群算法.该算法首先通过模拟食物源(目的地点)散发出的一种吸引蚂蚁不断向其靠近的香味素,使蚂蚁的搜索具有指向性;然后根据拥堵系数将路网中的道路分为不同的等级,并结合动态的分级策略防止算法陷入早熟.实验结果表明:本文算法比传统蚁群算法在最优解的质量及稳定性方面具有一定的优势.

Ant Colony Optimization （ACO） had not a well efficiency and ignored the traffic jam which was a serious problem in the real traffic, so it was hard to obtain a good solution. Therefore, a highly efficient ACO was proposed. First, the destination emitted fragrance information which could attract ants close to the destination and make the search of ants have directivity; Second, the route in road networks was classified, and dynamic classification strategy was combined to avoid the shortcoming such as stagnation. Result shows that this algorithm is better than ACO algorithm in the stability and quality of optimum solution.