Search - local search algorithm

Search - local search algorithm - List

[Software Engineering] P2P_query_arithmetic DL : 0: 查询扩展是解决信息获取领域中用词歧义性问题的关键技术,并被广泛应用于搜索引擎中,获得了巨大的成功.然而,由于P2P(peer-to-peer)系统是一个分散的、动态的系统,在P2P 环境下进行有效的查询扩展具有一定的挑战性.首先,利用查询与文档的关联关系构建了LEM(local expansion method)查询扩展方法然后,基于查询与文档用词的直接关联,提出了HEM(history_based expansion method)查询扩展方法.在此基础上,提出了一种基于查询扩展的混合P2P 环境下的搜索算法.实验及分析结果表明,查询扩展及其搜索算法能够极大地提高搜索的效果.-Query expansion is the area of information acquisition to resolve ambiguity in the wording of the key technologies, and widely used search engine, was a great success. However, as P2P (peer-to-peer) system is a decentralized, dynamic system, in the P2P environment for effective query expansion has certain challenges. First of all, the use of query and document relationship built LEM (local expansion method) and then query expansion method, based on the query terms and documents directly related to, put forward the HEM (history_based expansion method) methods of query expansion. On this basis, we proposed a query expansion based on a hybrid P2P search algorithm environment. Experiment and analysis results show that query expansion and its search algorithm can greatly improve the search results.
Date : 2026-01-02 Size : 176kb User : YouToo22
[Software Engineering] 1234255 DL : 0: 介绍了一种利用量子行为粒子群算法(QPSO)求解多峰函数优化问题的方法。为此，在 QPSO中引进一种物种形成策略，该方法根据群体微粒的相似度并行地分成子群体。每个子群体是围绕一个群体种子而建立的。对每个子群体通过QPSO算法进行最优搜索。从而保证每个峰值都有同等机会被找到，因此该方法具有良好的局部寻优特性。将基于物种形成的QPSO算法与粒子群算法(PSO)对多峰优化问题的结果进行比较。对几个重要的测试函数进行仿真实验结果证明，基于物种形成的QPSO算法可以尽可能多地找到峰值点，峰值收敛性能优于PS-A quantum behaved particle swarm optimization (QPSO) method for solving multimodal function optimization problems. For this reason, the introduction of a species in QPSO form a strategy, the method is based on the similarity of the groups of particles divided into sub-groups in parallel. Each sub-group is established around the seeds of a group. The QPSO algorithm optimal search for each sub-group. In order to ensure that each peak has the same opportunity to find, this method has good local optimization features. Will compare the results of multimodal optimization problems based on QPSO algorithm and particle swarm optimization (PSO) species formed. Simulation results prove that several important test function, based species formed QPSO algorithm can be as much as possible to find the peak point, the peak convergence outperforms PS
Date : 2026-01-02 Size : 335kb User : zhuifenger
[Software Engineering] sgrasp DL : 0: This paper is a survey of greedy randomized adaptive search procedures (GRASP). GRASP is a multi-start or iterative procedure where each GRASP iteration con- sists of a construction phase, where a feasible solution is constructed, followed by a local search procedure that finds a locally optimal solution. The construction phase of GRASP is essentially a randomized greedy algorithm. Repeated applications of the construction procedure yields diverse starting solutions for the local search. We review a basic GRASP, followed by enhancements to the basic procedure. We conclude by surveying operations research and industrial applications of GRASP.
Date : 2026-01-02 Size : 104kb User : Nydiron
[Software Engineering] image-feature DL : 0: 把 SIFT 算法应用在牙齿模型图像上，检测牙齿图像的特征点。方法：首先采用高斯差分算子 DoG 搜索整个图像的尺度和位置信息，从而确定具有代表性尺度、方向的特征点。基于其稳定性选择关键点，得到一个详细的模型以确定每个候选点的合适位置和范围。基于局部图像梯度方向信息将方向矢量和关键点对应起来。在选定范围内的每个关键点周边区域测量局部图像梯度，并采用 KNN 算法进行特征匹配。结果：通过大量的实验和与其他特征提取方法相比较，该方法能有效地检测牙齿模型图像的特征，并为牙齿模型三维重建提供有效的参数。-SIFT algorithm is applied to the teeth of the model image, the image feature point detecting teeth. Methods: DoG Gaussian differential operator to search the entire image the scale and location information, to determine a representative scale, the direction of the feature point. Select the key points based on their stability, to get a detailed model to determine the appropriateness of each candidate point location and extent. Information based on local image gradient direction and key points of the direction vectors correspond. Within the selected area around each critical point of measuring the local image gradient, and using KNN algorithm for feature matching. Results: Through a lot of experiments with other feature extraction methods and compare the proposed method can effectively detect tooth model image feature, and to provide an effective three-dimensional reconstruction tooth model parameters.
Date : 2026-01-02 Size : 968kb User : 焦婷
[Software Engineering] ACO-TSP DL : 0: 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.
Date : 2026-01-02 Size : 14kb User : sorayya
[Software Engineering] The-Systematic-Trajectory-Search-Algorithm-for-Fe DL : 0: In this paper we present the systematic trajectory search algorithm (STSA) which first globally explores the solution space then makes thorough local searches in promising areas. The STSA has been tested on training feedforward neural networks to solve the n-bit parity problem of various sizes and two real medical diagnosis problems. The experimental results show that the feedforward neural networks trained by the proposed algorithm have very good classification ability.
Date : 2026-01-02 Size : 333kb User : samir