中文摘要：

蚁群优化是一种元启发式的随机搜索技术，是目前解决组合优化问题最有效的工具之一．将信息素更新和随机搜索机制的改进相结合，提出一种快速求解旅行商问题的蚁群算法．首先给出了一种新的信息素增量模型，以体现蚂蚁在不同路径上行走时所产生的信息素差异；然后以蚂蚁经过的路径（直线段）作为信息素扩散浓度场的信源，改进了信息素扩散模型，强化了蚂蚁间的协作和交流；最后采用较低复杂度的变异策略对迭代的结果进行优化．在大量通用数据集上的实验表明，该算法不仅能获得更好的最优解，而且收敛速度有显著的提高．

英文摘要：

Ant colony optimization （ACO） is a population-based metaheuristic technique to solve combination optimization problems effectively, such as traveling salesman problem （TSP）, multidimensional knapsack problem （MKP）, and so on. However, how to improve the performance of ACO algorithms is still an active research topic. Though there are many algorithms solving TSPs effectively, there is an application bottleneck that the ACO algorithm costs too much time in order to get an optimal solution. Combining the pheromone updating with an improvement of the stochastic search strategy, a fast ACO algorithm for solving TSPs is presented in this paper. Firstly, a new pheromone increment model called ant constant, which keeps energy conversation of ants, is introduced to embody the pheromone difference of different candidate paths. Meanwhile, a pheromone diffusion model, which is based on info fountain of a path, is established to reflect the strength field of the pheromone diffusion faithfully, and it strengthens the collaboration among ants. Finally, a simple mutation strategy with lower computational complexity is adopted to optimize the evolution result. Experimental results on different benchmark data sets show that the proposed algorithm can not only get better optimal solutions but also enhance greatly the convergence speed.