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高分急求英语翻译，最好今晚24点以前发至邮箱：545995256@qq.com PS：请勿直接复制网上翻译！先谢谢了！

蚁群算法是最近几年才提出来的一种新的仿生优化算法，它是由意大利学者M.Dorigo, V.Mahiezzo, A.Colorni 等人受自然界中真实蚂蚁群体寻找食物过程的启发而率先提出来的，并成功地应用于求解一系列NP完全的组合优化问题，该算法具有较强的鲁棒性、分布式计算、易于与其它方法结合等优点。作为一种新型的模拟进化优化方法，蚁群算法在很多领域开始发挥作用，如工件排序问题，物流配送车辆路径选择问题等均可以用该算法进行优化解决，其应用前景非常广泛。
旅行商问题（Traveling Salesman Problem，TSP）是近代组合优化领域的一个典型难题。现实生活中的很多问题都可以转化为 TSP 问题，目前求解SP问题的精确算法已较为成熟，本文概述了求解TSP问题的几类算法，重点在于蚁群算法在TSP问题中的应用。在分别研究了TSP问题模型与蚁群算法数学模型的基础上，将二者结合起来给出了基于蚁群算法求解TSP问题的模型，针对这一基本模型中存在的问题，如收敛速度慢，易于陷入局部最优解，导致停滞现象等问题，本文总结了一个改进的最大-最小蚁群算法，主要是限制了信息素的取值范围，修改了信息素的更新方式，避免陷入局部最优或早熟收敛，防止出现一些路径上的信息素浓度远高于其他边的而导致的停滞情况，也避免了在算法运行过程中信息素轨迹的差异过大等问题。

Answer 1

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Ant colony algorithm is proposed only in recent years, a new optimization algorithm, which is the Italian scholar M. Dorigo, V. Mahiezzo, A. Colorni, who by the nature of real ants to find food groups inspired by the first process put forward and successfully applied to solve a series of NP complete combinatorial optimization problem, the algorithm has strong robustness, distributed computation, easy-to-the advantages of combining with other methods. As a novel simulated evolutionary optimization, ant colony began to play a role in many fields, such as the scheduling problem, logistics and vehicle routing problem by renewable solution used to optimize the algorithm and its application prospect is very extensive.
TSP (Traveling Salesman Problem, TSP) is the modern field of a typical combinatorial optimization problem. Many real life problems can be transformed into TSP problem is the exact algorithm to solve the problem SP has a more mature, this article outlines the types of problem solving TSP algorithm, ant colony algorithm focuses on the application of the TSP problem. TSP problems were studied in the model and the ant colony algorithm based on mathematical models, the two together gives the TSP problem based on ant colony algorithm for the model, the basic model for problems such as slow convergence and easy to fall into local optimal solution, leading to stagnation and other issues, this paper summarizes an improved max - min ant colony algorithm, the main factors limiting the range of information, modify the pheromone are updated, to avoid falling into local optimum premature convergence or to prevent some pheromone on the path is far higher than the other side of the stagnation caused by, but also to avoid running the algorithm tracks the differences in the pheromone is too large and so on.