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Description: 离散二进制粒子群优化算法，非常热门的新算法，给出几个函数实例，供大家参考-discrete binary PSO algorithm, a very popular new algorithm, gave a few examples of function, for your reference
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Description: GUI界面，proj.m 图片选择窗口，testListBoxlj.m 最优化算法,PSO.m,POWELL.m 互信息尺度,MI.m,EMI.m,RMI.m,HiMI.m,GMI.m,FPMI.m POWELL.m附属子程序oneDimSearch.m PSO.m附属子程序myMI.m 配准结果检验程序restore.m gobad.m对图像进行“平移”和“旋转” -GUI interface proj.m Photo selection window, testListBoxlj.m optimization algorithm, PSO.m. POWELL.m mutual information scale, MI.m, EMI.m, RMI.m, HiMI.m. GMI.m. FPMI.m POWELL.m subsidiary subroutine oneDimSearch.m PSO.m subsidiary Registration subroutine myMI.m results of testing procedures restore.m gobad.m right image "- shift "and" revolving "
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Author: 葛雪森 |
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Description: 一，
GUI界面，proj.m
图片选择窗口，testListBoxlj.m
二,最优化算法
PSO.m
POWELL.m
三，互信息尺度
MI.m
EMI.m
RMI.m
HiMI.m
GMI.m
FPMI.m
四，POWELL.m附属子程序
oneDimSearch.m
五，PSO.m附属子程序
myMI.m
六，配准结果检验程序
restore.m
七，
gobad.m对图像进行“平移”和“旋转”-First, GUI interface, proj.m picture selection window, testListBoxlj.m II optimization algorithm PSO.mPOWELL.m three or four MI.mEMI.mRMI.mHiMI.mGMI.mFPMI.m-scale mutual information, POWELL.m subsidiary sub- oneDimSearch.m five procedures, PSO.m subsidiary Subroutine myMI.m six, matching the results of testing procedures restore.m 7, gobad.m images
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Size: 19456 |
Author: 刘长乐 |
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Description: 基于混沌粒子群与模拟退火优化算法的最小二乘支持向量机参数自选择方法，是英文-Based on Chaotic Particle Swarm Optimization Algorithm and Simulated Annealing of least squares support vector machine parameters of self-selection method, is in English
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Size: 343040 |
Author: 毛毛 |
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Description: 以下是本文件夹说明
一，GUI界面，proj.m
图片选择窗口，testListBoxlj.m
二,最优化算法
三，互信息尺度
四，POWELL.m附属子程序
五，PSO.m附属子程序
六，配准结果检验程序
gobad.m对图像进行“平移”和“旋转”-The following is a description of the folder 1, GUI interface, proj.m picture selection window, testListBoxlj.m II optimization algorithm III, IV-scale mutual information, POWELL.m subsidiary Subroutine five, PSO.m subsidiary Subroutine six, with prospective results of testing procedures gobad.m images
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Size: 19456 |
Author: 羊于 |
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Description: 修正过的pso 算法 属于离散型的pso程序，可用于变量筛选，效果不错！-Modified discrete PSO algorithm belonging to the PSO procedure, can be used for variable selection, good results!
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Size: 1024 |
Author: 曹洪印 |
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Description: 时变参数的粒子群优化的matlab实现，可用于解决多种连续优化问题-A flexible implementation of PSO algorithm with time-varying parameters. Algorithm is suitable for solving continuous optimization problems. Special care has been taken to enable flexibility of the algorthm with respect to its parameters and to initial population selection. Detailed logging of the algorithm performance during the search is also enabled.
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Size: 7168 |
Author: 大长今 |
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Description: 用Java实现的进化算法包。包括遗传算法、粒子群算法、memetic算法和进化策略算法。-evolutionary-algorithm
Evolutionary Algorithm package implemented using Java.
The package serves as a foundation class library, supporting the implementation many variants of Evolutionary Algorithms, currently including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Memetic Algorithm (MA), Evolution Strategy (ES).
Highlighted features
· Support both binary & real-coded string representations of solution
· Operator-based design for flexibility
· EA Operators: Selection, Crossover, Mutation, Move operators in PSO & and the adaptive scheme in EA
· Individual learning: Davidon–Fletcher–Powell (DFP) and Davies, Swann, and Campey with Gram-Schmidt orthogonalization (DSCG) strategies and Random Mutation Hill-climbing (RMHC)
In addition, algorithm pipeline which is specified by XML file is also provided for practitioner to configure & design evolutionary algorithms at ease. User can edit runtime & algorithm parameters in the configuration file (XML) & issue the co
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Author: 陈雷 |
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Description: svm model selection by pso
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Author: ngu |
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Description: 介绍了PSO算法，以及与遗传算法/人工神经网络的区别，并介绍了算法中参数的选取方法等内容。-Introduced the PSO algorithm, as well as with genetic algorithm/artificial neural network differences and describes the algorithm parameter selection methods and so forth.
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Size: 4096 |
Author: shandong |
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Description: 就是相当简单的介绍一下物流选址方面的相关问题-The brief is quite simple logistics issues related to site selection
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Author: 沈海剑 |
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Description: A distributed PSOSVM hybrid system with feature selection and parameter optimization
-Abstract
This study proposed a novel PSO–SVM model that hybridized the particle swarm optimization (PSO) and support vector machines (SVM) to
improve the classification accuracy with a small and appropriate feature subset. This optimization mechanism combined the discrete PSO with the
continuous-valued PSO to simultaneously optimize the input feature subset selection and the SVM kernel parameter setting. The hybrid PSO–SVM
data mining system was implemented via a distributed architecture using the web service technology to reduce the computational time. In a
heterogeneous computing environment, the PSO optimization was performed on the application server and the SVM model was trained on the
client (agent) computer. The experimental results showed the proposed approach can correctly select the discriminating input features and also
achieve high classification accuracy.
# 2007 Elsevier B.V. All rights reserved.
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Author: alice |
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Description: 多目标优化
相对传统多目标优化方法, PSO在求解多目标问题上具有很大优势。首先, PSO的高效搜索能力有利于得到多目标意义下的最优解 其次, PSO通过代表整个解集的种群按内在的并行方式同时搜索多个非劣解,因此容易搜索到多个Pareto 最优解 再则, PSO的通用性使其适合于处理所有类型的目标函数和约束 另外, PSO 很容易与传统方法相结合,进而提出解决特定问题的高效方法。就PSO 本身而言,为了更好地解决多目标优化问题,必须解决全局最优粒子和个体最优粒子的选择问题-Compared with the traditional multi-objective optimization of multi-objective optimization method, PSO in solving multi-objective problem has a great advantage. First, PSO is conducive to the efficient search capabilities are more objective sense of the optimal solution Secondly, PSO representative of the entire solution set through the population by way of the inherent parallel search multiple non-inferior solution, and this can easily search for the most number of Pareto optimal solution Furthermore, PSO' s versatility make it suitable for handling all types of objective function and constraints addition, PSO is easy to integrate with the traditional method, and then propose an efficient way to solve specific problems. The PSO itself, in order to better address the multi-objective optimization problems, the need to address the global best particle and the individual selection of the optimal particle
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Author: 杨科 |
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Description: 提出了一种基于改进型微粒群算法的无线传
感器网络分簇路由算法来优化分簇过程。簇首节点的选取综合考虑候选节点和邻居节点的状态信息-Proposed a modified particle swarm algorithm based on wireless sensor network clustering routing algorithm to optimize the clustering process. Cluster head node, considering the selection of the candidate node and the neighbor node state information
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Author: Sherry |
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Description: Feature selection methods for machine learning algorithms such as SVR, including one filter-based method (CFS) and two wrapper-based methods (GA and PSO). The gridsearch is for the grid search for the optimal hyperparemeters of SVR. The SVM_CV is for the k-fold cross-validation of SVR. All the programs are flexible and could be implemented by the users themselves.-Feature selection methods for machine learning algorithms such as SVR, including one filter-based method (CFS) and two wrapper-based methods (GA and PSO). The gridsearch is for the grid search for the optimal hyperparemeters of SVR. The SVM_CV is for the k-fold cross-validation of SVR. All the programs are flexible and could be implemented by the users themselves.
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Size: 6144 |
Author: Gang Fu |
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Description: 对微粒群算法结构的改进方案有很多种，对其可分类为：采用多个子种群；改进微粒学习对象的选取策略；修改微粒更新迭代公式；修改速度更新策略；修改速度限制方法、位置限制方法和动态确定搜索空间；与其他搜索技术相结合；以及针对多模问题所作的改进。-Structure of the particle swarm algorithm to improve the program there are many, its can be classified as: the use of multiple sub-populations improve the selection of learning objects strategy particles modified particles iterative update formula modify the speed update strategy modified method of speed limits, position limits methods, and dynamically determines the search space and other search technologies and multi-mode problem for the improvements made.
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Author: liufang |
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Description: In this paper, a multimodal image fusion algorithm based on multiresolution
transform and particle swarm optimization (PSO) is proposed.
Firstly, the source images are decomposed into low-frequency coefficients and
high-frequency coefficients by the dual-tree complex wavelet transform
(DTCWT). Then, the high-frequency coefficients are fused by the maximum selection
fusion rule. The low-frequency coefficients are fused by weighted average
method based on regions, and the weights are estimated by the PSO to gain
optimal fused images. Finally, the fused image is reconstructed by the inverse
DTCWT. The experiments demonstrate that the proposed image fusion method
can illustrate better performance than the methods based on the DTCWT, the
support value transform (SVT), and the nonsubsampled contourlet transform
(NSCT).
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Author: ishwarya |
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Description: 粒子群算法的代码,并且用优化算法给定的标准函数进行测试，参数的选取不是最好的，但具有一定参考性-Particle swarm algorithm of the code, and USES the optimization algorithm given standard function tests, the parameter selection is not the best, but has the certain reference
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Author: shitou |
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Description: 粒子群优化算法是一种基于群体智能的演化计算技术。与遗传算法相比，PSO没有遗传算法中的选择(Selection)、交叉(Crossover)、变异(Mutation)等操作，而是通过粒子在解空间追随最优的粒子进行搜索。(Particle Swarm Optimization (PSO) is an evolutionary computing technique based on group intelligence. Compared with the genetic algorithm, PSO has no selection, selection (crossover), mutation (Mutation) and other operations in the genetic algorithm, but through the particles in the solution space to follow the optimal particle search.)
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Author: lazyxiaoming
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Description: 压缩文件中包含三个文件，分别是：GA+PSO、GA_only和PSO_only三个程序包。程序是用MATLAB编写的遗传+粒子群算法、遗传算法、粒子群算法，每个程序中包括主程序、种群选择、自适应函数子函数。(The compressed file contains three files, which are GA PSO, ga_only and pso_only three program package. The program is genetic particle swarm algorithm, matlab prepared by the genetic algorithm, particle swarm algorithm, each program includes main program, population selection, adaptive function function.)
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Author: taiyuanwang
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