Abstract:

A new ACO（Ant Colony Optimization） algorithm based on pheromone increment and diffusion models is presented.First，in light of energy conversation and transform，the pheromone increment formula is revised to show the pheromone difference of different paths.Meanwhile，a pheromone diffusion model based on info fountain of a path is established to faithfully reflect the strength field of pheromone diffusion，it strengthens the collaboration among ants.The experimental results for many TSP problems demonstrate that the proposed algorithm can not only get much better solutions but also improve the speed of convergence.

Key words: ant colony optimization, energy conversation and transform, diffusion model, strength field

摘要：

文章提出一种新的基于信息素增量和扩散模型的蚁群算法。首先，基于能量守恒与转换定律对信息素的增量模型进行修正，以体现蚂蚁在不同路径上行走时所产生的信息量差异;其次，以蚂蚁经过的路径（直线段）作为信息素扩散浓度场的信源，改善了信息素扩散模型，强化了蚂蚁间的协作和交流。大量TSP（Traveling Salesman Problem）问题的实验表明：该算法不仅能获得更好的解，而且能加快算法的收敛速度。

关键词: 蚁群算法, 能量守恒与转换, 扩散模型, 浓度场