Description: 为求解大规模TSP 问题, 提出了并行人工免疫系统的塔式主从模型(TMSM), 和基于TMSM 的并行免疫记忆克隆选择算法(PIMCSA) TMSM
是粗粒度的两层并行人工免疫模型, 其设计体现了分布式的免疫响应和免疫记忆机制. PIMCSA 用疫苗的迁移代替了抗体的迁移, 兼顾了种群多样性的保持和算法的收敛速度. 与其他算法相比, PIMCSA 在求解精度和运行时间上都更具优势, 而且问题规模越大优势越明显. TMSM 很好地体现了免疫系统的特性, PIMCSA 是适合求解大规模复杂优化问题
的并行人工免疫算法, 具有良好的可扩展性.-This paper presents a parallel model termed as towerlike master slave model (TMSM) for artificial immune systems. Based on TMSM, the parallel immune memory clonal selection algorithm ( PIMCSA) is also designed for dealing with large scale TSP problems. TMSM is a two level coarse grained parallel artificial immune model with distributed immune response and dis
tributed immune memory. In PIMCSA, vaccines are extracted and migrated between populations rather than antibodies as has been done in parallel genetic algorithms, it is a good balance between the diversity maintenance of populations and the convergent speed of the algorithm. PIMCSA shows superiority over other compared approaches both in solution quality and computation time, and the
lager the problem size the more outstanding the predominance will be. TMSM is a good simulation of biological immune system, and PIMCSA is a parallel artificial immune algorithm with good extensibility, which is capable of solving large scale and c Platform: |
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Author:崔冰 |
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Description: 用matlab编写的求解10城市、31城市、70城市的TSP遗传算法，供大家学习只用，算法简洁明了，注释全，易理解。-Using matlab to solve 10 cities, 31 cities, 70 cities TSP genetic algorithm, only for them to learn the algorithm concise, notes the whole, easy to understand. Platform: |
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Author:panyuxia |
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Description: TSP问题（又名：旅行商问题，货郎担问题）遗传算法通用matlab程序 D是距离矩阵，n为种群个数 参数a是中国31个城市的坐标 C为停止代数，遗传到第 C代时程序停止,C的具体取值视问题的规模和耗费的时间而定 m为适应值归一化淘汰加速指数,最好取为1,2,3,4,不宜太大 alpha为淘汰保护指数,可取为0~1之间任意小数,取1时关闭保护功能,建议取0.8~1.0之间的值 R为最短路径,Rlength为路径长度- TSP problem (also known as: traveling salesman problem, traveling salesman problem) genetic algorithm generic matlab program D is the distance matrix, n is the population number of parameters a C coordinates of the 31 cities to stop algebra, genetic to C on behalf of the program stops, the specific values of C depending on the size and time-consuming, m alpha for the fitness value of the normalized phase-out acceleration index, and is best taken as 1, 2, 3, 4, and not too much eliminated the protection index, it is desirable for any decimal between 0 and 1, taking 1:00 off the protective function, it is recommended to take a value between 0.8 to 1.0 R for the shortest path, Rlength for the path length Platform: |
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Author:fuhai |
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Description: 采用遗传微粒群算法（GPSO）求解旅行商问题（TSP）的源代码。内附多个算例，本算法对于中小规模问题求解效率很高，对于大大规模问题则效率略低。如有任何疑问。
-Genetic particle swarm optimization (GPSO) for solving the traveling salesman problem (TSP) source code. Included several examples of this algorithm for small and medium scale problem solving efficiency is very high, slightly lower efficiency for large massive problem. If you have any questions. Platform: |
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Author:ackchengg |
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Description: 针对传统的算法如遗传算法、粒子群算法等在TSP问题上求解精确性和求解规模上都还有一定的不足，本文提出了一种基于动态规划思想的粒子群优化算法。该算法用动态规划的方法实现粒子间的信息交互和粒子的进化，并且将粒子群中的粒子按无标度信息指导网络拓扑图的方式进行连接。仿真结果表明该方法能有效地减小误差率，提高解的精确，同时还保持了较低的计算复杂度，具有良好的稳健性。-TSP problem solving for the traditional algorithms such as genetic algorithms, particle swarm optimization accuracy and solving the scale, there is a certain lack of a dynamic programming algorithm-based particle swarm optimization algorithm. The algorithm using dynamic programming method to achieve the evolution of information exchange between the particles and the particles, and particle swarm particle scale-free information guide network topology map to connect. The simulation results show that this method can effectively reduce the error rate and improve the solution precision, while maintaining a low computational complexity, good soundness. Platform: |
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Author:程徐 |
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Description: 该文包括了基于 Matlab 的遗传算法解决 TSP 问题的
基本说明 ， 并在文后附录了实现该算法的所有源代码 。 此代
码经过本人的运行 ， 没有发现错误 ， 结果比较接近理论最优
值，虽然最优路径图有点交叉。
因为本人才疏学浅 ， 本报告及源代码的编译耗费了本人
较多的时间与精力 ， 特收取下载 积分 ， 还请见谅 。 若有什么
问题，可以私信，我们共同探讨这一问题。
希望能对需要这方面的知识的人有所帮助！-Description: Description: Description: Description: This paper includes the basic description of the Matlab-based genetic algorithm to solve TSP problem, and all the source code for the algorithm in the appendix below. This code after my run, no errors are found, the results are close to the theoretical optimal value, although the optimal path diagram a little cross. I Caishuxueqian this report and the source code, compile cost himself more time and effort, special charge Download Credits, please forgive me. If there be any problem, private letter, we explore this issue. Hope to help the people who need this knowledge! Platform: |
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Author:小小 |
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Description: 多旅行商问题的遗传算法 环形 固定节点- MTSPOFS_GA Fixed Start Open Multiple Traveling Salesmen Problem (M-TSP) Genetic Algorithm (GA)
Finds a (near) optimal solution to a variation of the "open" M-TSP by
setting up a GA to search for the shortest route (least distance needed
for each salesman to travel from the start location to unique individual
cities without returning to the starting location)
Platform: |
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Author:pxl |
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Description: 基于遗传算法(GA)的多变量旅行商问题(TSP) ，MTSPV_GA Variable Multiple Traveling Salesman Problem (M-TSP) Genetic Algorithm (GA)
Finds a (near) optimal solution to a variation of the M-TSP (that has a
variable number of salesmen) by setting up a GA to search for the
shortest route (least distance needed for the salesmen to travel to
each city exactly once and return to their starting locations)
-Based on genetic algorithm (GA) multivariate traveling salesman problem (TSP), MTSPV_GA Variable Multiple Traveling Salesman Problem (M-TSP) Genetic Algorithm (GA) Finds a (near) optimal solution to a variation of the M-TSP (that has a variable number of salesmen) by setting up a GA to search for the shortest route (least distance needed for the salesmen to travel to each city exactly once and return to their starting locations) Platform: |
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Author:赵元 |
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Description: TSP_GA Traveling Salesman Problem (TSP) Genetic Algorithm (GA)
Finds a (near) optimal solution to the TSP by setting up a GA to search
for the shortest route (least distance for the salesman to travel to
each city exactly once and return to the starting city)
Summary:
1. A single salesman travels to each of the cities and completes the
route by returning to the city he started from
2. Each city is visited by the salesman exactly once- TSP_GA Traveling Salesman Problem (TSP) Genetic Algorithm (GA)
Finds a (near) optimal solution to the TSP by setting up a GA to search
for the shortest route (least distance for the salesman to travel to
each city exactly once and return to the starting city)
Summary:
1. A single salesman travels to each of the cities and completes the
route by returning to the city he started from
2. Each city is visited by the salesman exactly once Platform: |
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Author:XIAOMIN XU |
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Description: 遗传算法计算多旅行商问题：多个商人从多个城市出发各自返回自己的出发城市。-MTSPF_GA Fixed Multiple Traveling Salesmen Problem (M-TSP) Genetic Algorithm (GA) Finds a (near) optimal solution to a variation of the M-TSP by setting up a GA to search for the shortest route (least distance needed for three salesmen to travel the start location to individual cities
and back to the original starting place) Platform: |
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Author:jiweijie |
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Description: This is an implementation of a genetic algorithm that solves the traveling salesman problem, created as a part of an online course in artificial intelligence for game programming. Code written scratch, theoretical information on TSP and genetic algorithms obtained mostly online besides an introductory lecture. Source and readme can be foun-This is an implementation of a genetic algorithm that solves the traveling salesman problem, created as a part of an online course in artificial intelligence for game programming. Code written scratch, theoretical information on TSP and genetic algorithms obtained mostly online besides an introductory lecture. Source and readme can be foun Platform: |
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Author:exemist |
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Description: This code is aimed at solving the multiple depots multiple traveling salesman problem by using genetic algorithm.Finding a (near) optimal solution to a variation of the M-TSP (that has a variable number of salesmen) by setting up a GA to search for the shortest route (least distance needed for the salesmen to travel to each city exactly once and return to their starting locations). The salesmen originate a set of fixed locations, called depots.-This code is aimed at solving the multiple depots multiple traveling salesman problem by using genetic algorithm.Finding a (near) optimal solution to a variation of the M-TSP (that has a variable number of salesmen) by setting up a GA to search for the shortest route (least distance needed for the salesmen to travel to each city exactly once and return to their starting locations). The salesmen originate a set of fixed locations, called depots. Platform: |
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Author:魏蕖 |
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Description: 基于MATLAB，对TSP问题进行基于遗传算法的求解。TSP 问题是典型的NP完全问题，代码包括求解TSP问题的遗传算法源程序m文件和实例-Based on TSP, the MATLAB problem is solved by genetic algorithm. TSP problem is a typical NP complete problem, including code for solving TSP problem genetic algorithm source program m documents and examples Platform: |
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Author:涂超 |
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Description: 基于遗传算法的TSP算法。本案例以14个城市为例，寻找出一条遍历14个城市的最短路径。-TSP Algorithm Based on Genetic Algorithm. In this case, 14 cities, for example, to find a traversal of the 14 cities in the shortest path. Platform: |
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Author:lcy |
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Description: 此程序为TSP问题的遗传算法，程序可以完美运行，计算最短路径。(This procedure for the TSP problem by genetic algorithm, the program can run perfectly, the shortest path calculation.) Platform: |
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Author:smoonlighter
|
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Description: MTSP_GA Multiple Traveling Salesmen Problem (M-TSP) Genetic Algorithm (GA)
Finds a (near) optimal solution to the M-TSP by setting up a GA to search
for the shortest route (least distance needed for the salesmen to travel
to each city exactly once and return to their starting locations)(Multiple Traveling Salesmen Problem - Genetic Algorithm) Platform: |
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Author:mokangxin |
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Description: 假设在一个供求关系系统中，车辆从货源取货，配送到对应的若干配送点。车辆存在最大载货量，且配送可能有时间限制。需要合理安排取货时间，组织适当的行车路线，使用户需求得到满足，同时使某个代价函数最小，比如总工作时间最少、路径最短等。
可以看出TSP问题是VRP问题的一种简单特殊形式。因此，VRP也是一种NP hard 问题。
目前解决此种问题的办法有多种，主要以启发式算法为主。包括退火算法、遗传算法、蚁群算法、禁忌算法等，本文介绍两种比较简单算法：C-W节约算法和遗传算法。(Assume that in a supply and demand relationship system, the vehicle picks up the goods from the source and delivers them to the corresponding distribution points. There is a maximum amount of cargo in the vehicle and there may be a time limit for delivery. It is necessary to arrange the picking time reasonably, organize the appropriate driving route, and satisfy the user's needs, and at the same time minimize a certain cost function, such as the minimum total working time and the shortest path.
It can be seen that the TSP problem is a simple and special form of the VRP problem. Therefore, VRP is also an NP hard problem.
At present, there are many ways to solve this kind of problem, mainly based on heuristic algorithms. Including annealing algorithm, genetic algorithm, ant colony algorithm, tabu algorithm, etc. This paper introduces two simple algorithms: C-W saving algorithm and genetic algorithm.) Platform: |
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Author:HappyppaH |
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Search - The genetic algorithm for TSP