Search - TSP LOCAL

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[Other] TSP DL : 0: 　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
Date : 2025-12-30 Size : 6kb User : logspace
[Other] Parallel-genetic-algorithm DL : 0: 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.
Date : 2025-12-30 Size : 910kb User : 陈嘉鑫
[Other] TSP DL : 0: 1 以10/30个结点的TSP问题为例,用遗传算法加以求解；　2 掌握遗传算法的基本原理、各个遗传操作和算法步骤；　3 能求出问题最优解，若得不出最优解，请分析原因； 4 要求界面显示每次迭代求出的局部最优解和最终求出的全局最优解。-For example, 1 to 10/30 junction TSP problem with a genetic algorithm to solve 2 mastered the basic principles of the genetic algorithm, various genetic manipulation, and algorithm steps 3 can solve for the optimal solution, if was not optimal solutions, analyze the reasons 4 interface displays each iteration obtained local optimal solution and ultimately find the global optimum solution.
Date : 2025-12-30 Size : 3kb User : soli
[Other] chfortsp DL : 0: 采用爬山（Climbing hill）算法解决旅行者（TSP）问题。爬山算法是蒙特卡罗算法的一种，可能陷入局部最优解问题。-Using climbing (Climbing hill) algorithm solves Travelers (TSP) problem. Climbing algorithm is a Monte Carlo algorithm, may fall into local optimal solution of the problem.
Date : 2025-12-30 Size : 3kb User : mada
[Other] 蚁群算法 DL : 0: 本文对蚁群算法的基本理论以及在 TSP 问题中的应用进行了系统研究和 MATLAB 仿真。介绍了蚁群算法的基本原理、特点和算法的实现方法。.基本蚁群算法由于存在搜索时间长，易陷入局部最优解等突出缺点，使得求解效果不是很好。针对这些缺陷，提出了改进的蚁群算法（最大-最小蚂蚁系统）求解 TSP 问题。改进主要在于限制路径信息素浓度、信息素的初始值以及强调对最优解得利用这三个方面。(In this paper, the basic theory of ant colony algorithm and its application in TSP are studied systematically and simulated by MATLAB. This paper introduces the basic principle, characteristics and implementation of ant colony algorithm. The basic ant colony algorithm is not very good because of its long search time and easy to fall into the local optimal solution. Aiming at these defects, an improved ant colony algorithm (maximum minimum ant system) is proposed to solve TSP. The main improvements are to limit the concentration of pheromone, the initial value of pheromone and to emphasize the utilization of the optimal solution.)
Date : 2025-12-30 Size : 6kb User : 阳光1111111