Description: TSP算法，应用局部搜索法得到最优路径，已经经过优化。没有密码，可以使用，vc环境下使用。-TSP algorithm, using local search to be optimal path has been optimized. No password can be used, vc environments. Platform: |
Size: 1357824 |
Author:霍哲 |
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Description: TSP问题是组合优化中的经典问题。其解决方法有局部优化方法和一些启发式算法,局部搜索方法充分考虑问题
的邻域结构,遗传算法有很好的全局搜索能力,memetic算法把遗传算法和局部优化算法相结合,试验结果证明,能很好地解
决TSP问题。-TSP problem is a classic combinatorial optimization problem. Its solution has a number of local optimization methods and heuristic algorithms, local search methods take full account of the issue of neighborhood structure, genetic algorithm has good ability of global search, memetic algorithm for the genetic algorithm and local optimization algorithm combining test results proved that well positioned to solve the TSP problem. Platform: |
Size: 102400 |
Author:文龙 |
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Description: 将局部优化算子引入遗传算法求解TSP问题，以求提高算法的性能。具体措施是在标准遗传算法的最后阶段增加步，即对每代的最优个体进行一定次数的局部搜索，以求改善该最优个体。首先提出将反序一杂交法引入局部优化过程中。
同几种‘常用的局部优化力一法相比，反序一杂交法的性能最为突出。实验结果表明，该优化力一法能有效求解300个城市以内的
TSP问题。
-Will introduce a local optimization operator TSP problem genetic algorithm, in order to increase the performance of algorithm. Specific measures in the standard genetic algorithm to increase the final phase of step-by-step, that is optimal for each individual to carry out on behalf of a certain number of local search, in order to improve the best individual. First put forward the anti-sequence hybridization to introduce a local optimization process. With several ' common edge of a local optimization method, the anti-sequence of a hybridization of the most outstanding performance. Experimental results show that the optimization method can effectively force a solution of 300 cities within the TSP problem. Platform: |
Size: 43008 |
Author:JONE |
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Description: 模拟退火和对称
*欧几里德旅行商问题。
*
*为基础的解决办法的本地搜索启发式
*非过境道路和近邻
-/*
* Simulated annealing and the Symetric
* Euclidian Traveling Salesman Problem.
*
* Solution based on local search heuristics for
* non-crossing paths and nearest neighbors
*
* Storage Requirements: n^2+4n ints
*
* Problem: given the coordinates of n cities in the plane, find a
* permutation pi_1, pi_2, ..., pi_n of 1, 2, ..., n that minimizes
* sum for 1<=i<n D(pi_i,pi_i+1), where D(i,j) is the euclidian
* distance between cities i and j
*
* Note: with n cities, there is (n-1)!/2 possible tours.
* factorial(10)=3628800 factorial(50)=3E+64 factorial(150)=5.7E+262
* If we could check one tour per clock cycle on a 100 MHZ computer, we
* would still need to wait approximately 10^236 times the age of the
* universe to explore all tours for 150 cities.
*
* gcc-O4-o tsp tsp.c-lm tsp | ghostview-
*
* Usage: tsp [-v] [n=dd] [s=dd] [filename]
* -v : verbose
* n= : nb of cities (cities generated randomly on E^2 Platform: |
Size: 86016 |
Author:孙博 |
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Description: 提出一种改进的禁忌搜索算法来求解背包问题。该算法基于禁忌搜索技术,并采用I&D策略,同时设计了两种针对局
部最优解的变异算子。改进后的算法能有效地弥补标准禁忌算法对初始解依赖的缺陷,同时也避免了搜索停滞的现象。通过对具
体实例和随机问题的测试,表明改进后的禁忌搜索算法有更好的性能。
关-An improved tabu search algorithm to solve knapsack problem. The algorithm is based on tabu search techniques, using I & D strategies, while designed for the local optimal solution of the two kinds of mutation operator. The improved algorithm can effectively compensate for the standard tabu search algorithm depends on the initial solution defect, but also to avoid the phenomenon of search stagnation. Through specific examples and random-question test, indicating that the improved tabu search algorithm has better performance. Guan Platform: |
Size: 6144 |
Author:logspace |
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Description: 由Helsgaun改进的Lin-Kernighan局部启发搜索算法（LKH）求解大规模TSP问题，使用Java语言实现，具有强大的求解TSP性能。-Helsgaun improved the Lin-Kernighan local search algorithm (LKH) for solving large scale TSP problem, use the Java language, solving the TSP with a strong performance. Platform: |
Size: 788480 |
Author:new |
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Description: 经典遗传算法利用单一种群对种群个体进行交叉、变异和选择操作，在进化过程中的超级个体易产生过早收敛现象，粗粒度并行遗传算法利用多个子种群进行进化计算，各子群体分别独立进行遗传操作，相互交换最优个体后继续进化。该文证明了该算法的搜索过程是一个有限时齐遍历马尔柯夫链，给出粗粒度并行遗传算法全局最优收敛性证明。对于旅行商问题TSP利用粗粒度并行遗传算法进行了求解，以解决经典遗传算法的收敛到局部最优值问题。仿真结果表明，算法的收敛性能优于经典遗传算法。-Classic genetic algorithm using a single population of individuals in a population cross, mutation and selection operation, the super individuals in the evolutionary process is easy to produce premature convergence phenomenon, coarse-grained parallel genetic algorithm using multiple sub-populations of evolutionary computation, various sub-groups, respectively, independent The genetic manipulation, the exchange of best individual continue to evolve. This paper shows that the search process of the algorithm is a finite homogeneous traverse the Markov chain, given the coarse-grained parallel genetic algorithm global optimal convergence proof. For the traveling salesman problem TSP coarse-grained parallel genetic algorithm to solve to solve the classic genetic algorithm converges to a local optimum value. The simulation results show that the convergence of the algorithm is superior to the classical genetic algorithm. Platform: |
Size: 931840 |
Author:陈嘉鑫 |
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Description: 全书包括两部分共15章。第1章指出问题求解困难的原因；第2章简要介绍几本概念；第3-5章分别综述穷举搜索法、局部搜索法、贪婪法、分而治之法、动态规划法、分支定界法、模拟退火法和禁忌搜索法；第6-7章介绍一般演化算法的细节问题；第8-10章介绍如何采用演化方法求解TSP问题、处理约束条件以及算法调整；第11章讨论了环境和噪声问题；第12-13分别提供神经网络和模糊系统相关内容；第14章对混合系统和扩展演化算法做简短讨论；第15章介绍演化算法在实际问题中的应用，并给出有价值的提示。-The book includes two parts with 15 chapters. The first chapter points out the reasons for the problem solving difficult the second chapter briefly introduces some of the concepts Chapter 3-5 respectively in exhaustive search, local search, greedy, divide and rule method, dynamic programming, branch and bound method, simulated annealing and tabu search method 6-7 chapter details the general evolution algorithm how to 8-10 chapter introduces evolution method for solving TSP problem, constraint conditions and algorithm the eleventh chapter discusses the environment and the problem of noise the 12-13 respectively with neural network and fuzzy system related content the fourteenth chapter on the mixing system and evolution algorithm is briefly discussed the fifteenth chapter introduces the application of evolutionary algorithm in practical problems, and gives valuable hints. Platform: |
Size: 10104832 |
Author:alvin |
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Description: Ant Colony Optimization Algorithm (ACO) and TSP
ACO algorithm mimics the behavior of real life ants and on how they interact with each other. The basic philosophy of the algorithm involves the movement of a colony of ants through the different states of the problem influenced by two local decision policies, viz., trails and attractiveness and two mechanisms, viz., trail evaporation and daemon actions. The algorithm aims to search for an optimal path based on the behavior of ants seeking a path between their colony and a source of food. Thereby, each such ant incrementally constructs a solution to the problem. Platform: |
Size: 14336 |
Author:sorayya |
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Search - local search for tsp