搜索资源列表
pso
- 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!
tvpso
- 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 resp
java_evolutionary_algorithms
- 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 Algorith
PSO-VRP
- 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
SVMhybridsystem
- 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 clas
logisticsuanfa
- 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
Ipso
- 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 select
ection
- 粒子群优化算法的测试选择优化方法PSO optimization method of test selection-PSO optimization method of test selection
FeatureSelection_MachineLearning
- 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
pso
- 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-p
czf_blurcue
- 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 b
pso
- 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
Cooperations-in-PSO
- We de ne ve cooperation mechanisms in Particle Swarm Optimisation, loosely inspired by some models occurring in nature, and based on two quan- tities: a help matrix, and a reputation vector. We call these ve mechanisms, respectively, Reciproc
PSO
- 各种粒子群或改进型粒子群算法 1）粒子群优化算法(求解无约束优化问题) 1>PSO(基本粒子群算法) 2>YSPSO(待压缩因子的粒子群算法) 3>LinWPSO(线性递减权重粒子群优化算法) 4>SAPSO(自适应权重粒子群优化算法) 5>RandWSPO(随机权重粒子群优化算法) 6>LnCPSO(同步变化的学习因子) 7>AsyLnCPSO(异步变化的学习因子)(算法还有bug) 8>SecPSO(用二阶粒
03-Fixed-Feature-Selection-using-PSO
- Feature Selection Using PSO
PSO算法程序
- 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, P
GA+PSO
- 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 algo
code
- 是关于文章Self-Adaptive Particle Swarm Optimization for Large-Scale Feature Selection in Classification的Matlab源代码，希望对从事这个方向的人员有所帮助。(Self-Adaptive Particle Swarm Optimization for Large-Scale Feature Selection in Classification)
GA-PSO
- PSO算法计算函数极值时，常常出现早熟现象，导致求解函数极值存在较大的偏差，然而遗传算法对于函数寻优采用选择、交叉和变异算子操作，直接以目标函数作为搜索信息，以一种概率的方式来进行，因此增强了粒子群优化算法的全局寻优能力，加快了算法的进化速度，提高了收敛精度。(When PSO algorithm calculates function extremum, it often appears premature phenomenon, which leads to large deviation