Description: This demo nstrates the use of the reversible jump MCMC simulated annealing for neural networks. This algorithm enables us to maximise the joint posterior distribution of the network parameters and the number of basis function. It performs a global search in the joint space of the parameters and number of parameters, thereby surmounting the problem of local minima. It allows the user to choose among various model selection criteria, including AIC, BIC and MDL Platform: |
Size: 958464 |
Author:大辉 |
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Description: 应用遗传算法是被认为求解NP难题的有效手段之一,求解物流配送车辆路径优化问题时,在传统遗传算法的基础上,并引入了免疫算法的思想,实验结果表明该算法具有更好的全局和局部搜索能力和收敛速度,可有效地解决物流配送车辆路径优化问题。-Application of genetic algorithms to solve NP is considered an effective means of problem solving to optimize logistics and distribution vehicle routing problem, in the traditional genetic algorithm based on immune algorithm and the introduction of ideas, experimental results show that the algorithm has a better overall and local search ability and convergence speed, which can effectively solve the logistics and distribution VRP. Platform: |
Size: 7168 |
Author:王博文 |
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Description: SCE（shuffled complex evolution ）是一种相对较新的连续性问题的元启发搜索算法。非常适合于求解具有多个局部最小的全局优化问题。SCE算法的主要特征是通过竞争进化和定期洗牌来确保每个复形获得的信息能在整个问题空间获得共享。-SCE (shuffled complex evolution) is a relatively new meta-continuity heuristic search algorithm. Very suitable for solving with multiple local minimum of the global optimization problem. SCE algorithm is characterized primarily by evolution through competition and regular cards to ensure that each complex information obtained in the whole question of access to shared space. Platform: |
Size: 9216 |
Author:胡军 |
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Description: 针对多椭圆检测问题提出了一种快速随机检测算法。该算法利用在图像中随机采样到的一个边缘点和
局部搜索到的两个边缘点以及这三个点的邻域信息确定候选椭圆,再将候选椭圆变换为对应圆,通过确认真圆来确
认真椭圆。在确定候选椭圆时,最大限度地减少随机采样点数‰ 剔除更多的非椭圆点,降低了无效采样,减少了无效
计算。数值实验结果表明:该算法具有良好的鲁棒性,其检测速度比同类算法快-Ellipse detection problem for many a fast random detection algorithm. The algorithm uses random sampling in the image of an edge point and the local search to the two edge points, as well as the three-point neighborhood information to determine the candidate ellipse, and then transformed into the corresponding elliptical candidate won, through the identification of real yen to confirm really elliptical. In determining when a candidate ellipse, to minimize random sampling points ‰ remove more non-oval points, reducing invalid sampling, to reduce the calculation invalid. Numerical experimental results show that: the algorithm has good robustness, and its detection faster than similar algorithms quickly Platform: |
Size: 526336 |
Author:刘镖峰 |
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Description: 禁忌搜索（Tabu search）是局部邻域搜索算法的推广，Fred Glover在1986年提出这个概念，进而形成一套完整算法。
-Tabu search (Tabu search) is a local neighborhood search algorithm for the promotion, Fred Glover in 1986, proposed this idea, thus forming a complete set of algorithms. Platform: |
Size: 578560 |
Author:小龙 |
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Description: 基于模拟退火的粒子群算法，模拟退火算法在搜索过程中具有该概率突跳的能力，能够有效地避免搜索过程陷入局部极小解。-Based on simulated annealing particle swarm optimization, simulated annealing algorithm in the search process has a sudden jump in the probability of the capacity, which can effectively avoid the search process into a local minimum solution. Platform: |
Size: 3072 |
Author:cuiping5122 |
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Description: 经典微粒群算法MATLAB程序，通过修改w值使得微粒群搜索在全局搜索与局部搜索之间达到一个较好的平衡-Classical PSO MATLAB program, by modifying the value of w makes particle swarm search in the global search and local search to achieve a better balance between Platform: |
Size: 2048 |
Author:董飞 |
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Description: In computer science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. PSO optimizes a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae over the particle s position and velocity. Each particle s movement is influenced by its local best known position and is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions. Platform: |
Size: 2048 |
Author:suci ariani |
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Description: 大规模图像检索的代码，matlab与c++混合编程。总结了目前图像检索领域目前主要存在的方法。通过阅读该代码，可以对于经典的“词袋”模型（bow模型）有个具体的了解，但是该代码没有提供前序的特征提取，是直接从对提取好的特征向量聚类开始的，包括了k-means，分层k-means（HKM）聚类，倒排文件的建立和索引等，该代码还提供了局部敏感哈希（LSH）方法。最后，这份代码是下面这篇论文的作者提供的，
Indexing in Large Scale Image Collections: Scaling Properties and Benchmark-This C++/Matlab package implements several algorithms used for large scale
image search. The algorithms are implemented in C++, with an eye on large
scale databases. It can handle millions of images and hundreds of millions
of local features. It has MEX interfaces for Matlab, but can also be used
(with possible future modifications) from Python and directly from C++. It
can also be used for approximate nearest neighbor search, especially using
the Kd-Trees or LSH implementations.
The algorithms can be divided into two broad categories, depending on the
approach taken for image search:
1. Bag of Words:
----------------
The images are represented by histograms of visual words.
It includes algorithms for computing dictionaries:
* K-Means.
* Approximate K-Means (AKM).
* Hierarchical K-Means (HKM).
It also includes algorithms for fast search:
* Inverted File Index.
* Inverted File Index with Extra Information (for example for implementing
Hamming Embedding).
* Platform: |
Size: 148480 |
Author:薛振华 |
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Description: Matlab PSO
In computer science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. PSO optimizes a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae over the particle s position and velocity. Each particle s movement is influenced by its local best known position but, is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions Platform: |
Size: 1024 |
Author:Bekhouche |
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Description: 遗传算法 ( Genetic Algorithm , GA) 是借鉴生物界自然选择和群体进化机制形成的一种全局寻优算法 。与传统的优化算法相比 ,遗传算法具有如下优点 [1 ] :1 ) 不是从单个点 ,而是从多个点构成的群体开始搜索 2) 在搜索最优解过程中 ,只需要由目标函数值转换得来的适应值信息 ,而不需要导数等其它辅助信息 3) 搜索过程不易陷入局部最优点 。
数学建模中常用的matlab算法，遗传算法，内容详细，包括PDF版本的详细的算法实现过程；-Genetic Algorithm (based Algorithm, GA) is using biological natural selection and group evolution mechanism to form a global optimization Algorithm is proposed. Compared with the traditional optimization algorithm, genetic algorithm has the following advantages [1] : 1) is not a single point, but multiple points of groups began to search 2) in the process of searching the optimal solution, only needs to be derived the objective function value of fitness information, without the need for a derivative and other auxiliary information 3) the search process is not easy to fall into local optimal point.
Mathematical modeling of the commonly used matlab algorithm, genetic algorithm, and content in detail, including the PDF version of the detailed algorithm implementation process Platform: |
Size: 163840 |
Author:刘珅 |
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Description: 蚁群算法是一种智能优化算法，通过介绍蚁群觅食过程中基于信息素的最短路径的搜索策略，给出基于MATLAB的蚁群算法在旅行商问题中的源代码m文件，对问题求解进行局部优化。-Ant colony algorithm is an intelligent optimization algorithm through the shortest route of pheromone search strategy based on the ant foraging process, given the ant colony algorithm based on MATLAB in the traveling salesman problem M source code files, local optimization to solve the problem. Platform: |
Size: 7168 |
Author:涂超 |
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Description: 改进的粒子群算法(PSO)MATLAB源程序m文件，在粒子群算法中引入克隆、选择算子寻求最优解。在同一粒子周围使用克隆选择算子进行多个方向的全局和局部搜索，促使种群中粒子快速进化，较快的得到局部最优和全局最优的位置-Improved particle swarm optimization algorithm (PSO) MATLAB source M files, in the particle swarm optimization algorithm to clone, the operator to find the optimal solution. The global and local search of the multiple directions using clonal selection operator around the same particle, which promotes the rapid evolution of the particles in the population, and get the local optimal and the global optimum. Platform: |
Size: 4703232 |
Author:涂超 |
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Description: In computer science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae over the particle's position and velocity. Each particle's movement is influenced by its local best known position, but is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions. Platform: |
Size: 786432 |
Author:studwzq
|
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Description: Particle Swarm Optimization (PSO) is a newly-emerging heuristic global search algorithm based on swarm intelligence and it searches the global optimal point in the complex search space through the competition and collaboration of the particles; however, PSO is easy to get trapped in local extremum, to have premature
convergence or stagnation. In order to help PSO strike a balance between individual diversity and swarm convergence, this paper proposes an artificial immune PSO based on clonal selection. It integrates the advantages of artificial immunity and PSO and introduces the idea of immunity in PSO, namely to add immune operator in PSO so as to make PSO a new algorithm with the function of immunity. Platform: |
Size: 250880 |
Author:Dallaki |
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Description: 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.) Platform: |
Size: 6144 |
Author:阳光1111111 |
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Search - local search in matlab