Search - local search algorithm

Search - local search algorithm - List

[Windows Develop] dye-local-search DL : 0: Local search. Algorithm that explores the space of possible solutions in sequential fashion, moving from a current solution to a \"nearby\" one. Neighbor relation. Let S  S be a neighbor relation for the problem. Gradient descent. Let S denote current solution. If there is a neighbor S of S with strictly lower cost, replace S with the neighbor whose cost is as small as possible. Otherwise, terminate the algorithm
Date : 2008-10-13 Size : 204.57kb User : supercat188
[Windows Develop] dye-local-search DL : 0: Local search. Algorithm that explores the space of possible solutions in sequential fashion, moving from a current solution to a "nearby" one. Neighbor relation. Let S  S be a neighbor relation for the problem. Gradient descent. Let S denote current solution. If there is a neighbor S of S with strictly lower cost, replace S with the neighbor whose cost is as small as possible. Otherwise, terminate the algorithm-Local search. Algorithm that explores the space of possible solutions in sequential fashion, moving from a current solution to a nearby one.Neighbor relation. Let S
Date : 2026-01-02 Size : 204kb User : supercat188
[Windows Develop] tspyouhua DL : 0: 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.
Date : 2026-01-02 Size : 42kb User : JONE
[Windows Develop] sfsf 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 : 2026-01-02 Size : 244kb User : logspace
[Windows Develop] Stochastic_and_Global_Optimization DL : 0: We present an algorithm for finding the global minimum of multimodal functions. The proposed algorithm is based on differential evolution (DE). Its distinguishing features are that it implements pre-calculated differentials and that it suitably utilizes topographical information on the objective function in deciding local search.
Date : 2026-01-02 Size : 4.64mb User : regenrentgen
[Windows Develop] GNew_Genetic_e DL : 0: 遗传算法及其育种:GA于20世纪60年代由美国Michigan大学J.H.Holland教授[1]首先提出。它可广泛应用于人工智能、机器学习、函数的优化、自动控制等领域。GA的突出特点是将问题的解空间间通过编码转换为GA的搜索空间，把问题的解转换为生物的个体，并借助生物的遗传和进化理论，对多个个体同时进行选择、交叉和变异操作。这样，可以较快地搜索到最优解。但是，遗传算法易陷入局部最优。搜索效率还不是 -Genetic Algorithm and Breeding: GA 1960s first proposed by the University of Michigan, USA JHHolland professor [1]. It can be widely used in artificial intelligence, machine learning, optimization, automatic control functions. The salient features of the GA is the solution space of the problem by transcoding the GA search space, the solution of the problem of biological individuals, and with the help of bio-genetic and evolutionary theory, multiple individual selection, crossover and variation operation. In this way, you can quickly search for the optimal solution. However, the genetic algorithm is easily trapped into local optima. Search efficiency is not
Date : 2026-01-02 Size : 707kb User : chodayy
[Windows Develop] MLNewton-Raphson DL : 0: Maximum Likelihood Local search scheme: Newton-Raphson algorithm-Maximum Likelihood Local search scheme: Newton-Raphson algorithm
Date : 2026-01-02 Size : 1kb User : jorgehas