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[Other resource] 差别算法matlab源码

Description: 粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation).源于对鸟群捕食的行为研究 PSO同遗传算法类似，是一种基于叠代的优化工具。系统初始化为一组随机解，通过叠代搜寻最优值。但是并没有遗传算法用的交叉(crossover)以及变异(mutation)。而是粒子在解空间追随最优的粒子进行搜索。详细的步骤以后的章节介绍 同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域-Particle Swarm Optimization (PSO) is an evolutionary technology (evolutionary computation). Predatory birds originated from the research PSO with similar genetic algorithm is based on iterative optimization tools. Initialize the system for a group of random solutions, through iterative search for the optimal values. However, there is no genetic algorithm with the cross - (crossover) and the variation (mutation). But particles in the solution space following the optimal particle search. The steps detailed chapter on the future of genetic algorithm, the advantages of PSO is simple and easy to achieve without many parameters need to be adjusted. Now it has been widely used function optimization, neural networks, fuzzy systems control and other genetic algorithm applications
Platform: | Size: 16633 | Author: 张正 | Hits:

[AI-NN-PR] 差别算法matlab源码

Description: 粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation).源于对鸟群捕食的行为研究 PSO同遗传算法类似，是一种基于叠代的优化工具。系统初始化为一组随机解，通过叠代搜寻最优值。但是并没有遗传算法用的交叉(crossover)以及变异(mutation)。而是粒子在解空间追随最优的粒子进行搜索。详细的步骤以后的章节介绍 同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域-Particle Swarm Optimization (PSO) is an evolutionary technology (evolutionary computation). Predatory birds originated from the research PSO with similar genetic algorithm is based on iterative optimization tools. Initialize the system for a group of random solutions, through iterative search for the optimal values. However, there is no genetic algorithm with the cross- (crossover) and the variation (mutation). But particles in the solution space following the optimal particle search. The steps detailed chapter on the future of genetic algorithm, the advantages of PSO is simple and easy to achieve without many parameters need to be adjusted. Now it has been widely used function optimization, neural networks, fuzzy systems control and other genetic algorithm applications
Platform: | Size: 16384 | Author: | Hits:

[AI-NN-PR] 粒子群优化算法C

Description: 同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域-comparison with the genetic algorithm, the advantages of PSO is simple and easy to achieve without many parameters need to be adjusted. Now it has been widely used function optimization, neural networks, fuzzy systems control and other genetic algorithm applications
Platform: | Size: 16384 | Author: wxd | Hits:

[AI-NN-PR] ImprovedSVM

Description: 将遗传算法(GA)与传统SVM算法结合,构造出一种参数最优的进化SVM(GA2SVM),SVM 模型采用径向基函数(RBF)作为核函数，利用格雷码编码方式对SVM算法的模型参数进行遗传编码和优化搜索，将搜索到的优化结果作为SVM 的最终模型参数。-Genetic algorithm (GA) combined with the traditional SVM algorithm, a kind of tectonic evolution of the optimal parameters of SVM (GA2SVM), SVM model using Radial Basis Function (RBF) as kernel function, the use of Gray code encoding algorithm of the SVM model parameters of genetic coding and optimization of search, will search for the optimal results as the final SVM model parameters.
Platform: | Size: 179200 | Author: zhaoxiufen | Hits:

[Special Effects] gpso

Description: 粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation)，有Eberhar博士和kennedy博士发明。源于对鸟群捕食的行为研究 ，PSO同遗传算法类似，是一种基于叠代的优化工具。 -Particle Swarm Optimization (PSO) is an evolutionary computation technique (evolutionary computation), and has Eberhar Dr. Dr. kennedy invention. Stems from the behavior of predatory birds, PSO with genetic algorithm is similar to an iterative optimization-based tools.
Platform: | Size: 2048 | Author: 叶开 | Hits:

[matlab] PSO-evolutionarycomputation

Description: 粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation)，有Eberhart博士和kennedy博士发明。源于对鸟群捕食的行为研究 PSO同遗传算法类似，是一种基于叠代的优化工具。系统初始化为一组随机解，通过叠代搜寻最优值。但是并没有遗传算法用的交叉(crossover)以及变异(mutation)。而是粒子在解空间追随最优的粒子进行搜索。详细的步骤以后的章节介绍 同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域-Particle Swarm Optimization (PSO) is an evolutionary computation technique (evolutionary computation), has Dr. Eberhart and Dr. kennedy invention. Deriving from the behavior of birds of prey PSO with genetic algorithm is similar to an iterative optimization-based tools. System initialization for a group of random solutions, through the iterative search for optimal values. But there is no cross-genetic algorithm used (crossover) and mutation (mutation). But the particles in the solution space of the particles to follow the optimal search. In detail the steps after the introduction sections compared with the genetic algorithm, PSO has the advantage of being simple and easy and did not realize many of the parameters need to be adjusted. Has been widely applied to function optimization, neural network training, fuzzy system control, as well as other genetic algorithm applications
Platform: | Size: 22528 | Author: zzh | Hits:

[MPI] PSOtoolbox

Description: 微粒群算法[PSO ] 是由Kennedy 和Eberhart等于1995 年开发的一种演化计算技术, 来源于对鸟群捕食过程的模拟。PSO同遗传算法类似，是一种基于叠代的优化工具，但与遗传算法使用遗传操作子进行优化不同，利用群体中各个体之间的“协作”与“竞争”关系，根据自身及其竞争者的飞行经验，调整自己的行为。同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域。-Particle Swarm Optimization [PSO] are equal by Kennedy and Eberhart in 1995 developed an evolutionary computing technology, from preying on the birds of the simulation process. PSO with genetic algorithm is similar to an iterative optimization-based tool, but the use of genetic algorithms and genetic manipulation of different sub-optimize the use of groups between the various entities within the " collaboration" and " competitive" relationship, according to themselves and their competition the flying experience, adjust their behavior. Comparison with genetic algorithms, PSO has the advantage of being simple and easy and did not realize the need to adjust the parameters much. Has been widely applied to function optimization, neural network training, fuzzy system control, as well as other genetic algorithm applications.
Platform: | Size: 883712 | Author: wzy | Hits:

[AI-NN-PR] java_evolutionary_algorithms

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
Platform: | Size: 104448 | Author: 陈雷 | Hits:

[Mathimatics-Numerical algorithms] PSO

Description: matlab 遗传算法GA，粒子群算法PSO,蚁群算法AS 前段时间上智能计算方法实验课上，自己做的程序。帖到这里，希望有人能改进它们，交流经验这样更有价值。 遗传算法解决最小生成树问题，PURFER编码。 粒子群算法做无约束最优化问题。 蚁群算法解决TSP问题。 如果有宝贵经验希望能交流一下，谢谢，-matlab genetic algorithm GA, particle swarm optimization PSO, ant colony algorithm for calculation of AS some time ago on the intelligent test classes, make their own procedures. Quote here, hope someone can improve them, and exchange of experience which is more valuable. Genetic algorithm to solve the minimum spanning tree problem, PURFER coding. Particle swarm optimization to do unconstrained optimization problem. Ant colony algorithm to solve the TSP problem. If there is hope to exchange our experience, thank you, He He
Platform: | Size: 5120 | Author: stephen | Hits:

[Software Engineering] PSO

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.
Platform: | Size: 4096 | Author: shandong | Hits:

[matlab] pso

Description: This an implementation of Particle Swarm Optimization algorithm using the same syntax as the Genetic Algorithm Toolbox, with some additional options specific to PSO. Allows code-reusability when trying different population-based optimization algorithms. Certain GA-specific parameters such as cross-over and mutation functions will not be applicable to the PSO algorithm. Demo function included, with a small library of test functions. Requires Optimization Toolbox.-This is an implementation of Particle Swarm Optimization algorithm using the same syntax as the Genetic Algorithm Toolbox, with some additional options specific to PSO. Allows code-reusability when trying different population-based optimization algorithms. Certain GA-specific parameters such as cross-over and mutation functions will not be applicable to the PSO algorithm. Demo function included, with a small library of test functions. Requires Optimization Toolbox.
Platform: | Size: 4096 | Author: Chris Leung | Hits:

[matlab] PSO

Description: Global Optimization Toolbox provides methods that search for global solutions to problems that contain multiple maxima or minima. It includes global search, multistart, pattern search, genetic algorithm, and simulated annealing solvers. You can use these solvers to solve optimization problems where the objective or constraint function is continuous, discontinuous, stochastic, does not possess derivatives, or includes simulations or black-box functions with undefined values for some parameter settings.
Platform: | Size: 1024 | Author: saswata | Hits:

[AI-NN-PR] TSP-based-on-improved-pso

Description: 基于对粒子群优化算法原理的分析，实现了一种基于TSP的改进的粒子群优化算法：求解TSP的混合粒子群算法，结合遗传算法、蚁群算法和模拟退火算法的思想来解决TSP问题。-Particle swarm optimization based on the principle of the analysis, implemented based on TSP, improved particle swarm optimization algorithm: solving the TSP hybrid particle swarm algorithm and genetic algorithm, ant colony algorithm and simulated annealing algorithm to solve the TSP problem the idea.
Platform: | Size: 633856 | Author: durongmao | Hits:

[AI-NN-PR] Particle-algorithm

Description: 粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation)，有Eberhart博士和kennedy博士发明。源于对鸟群捕食的行为研究。 PSO同遗传算法类似，是一种基于叠代的优化工具。系统初始化为一组随机解，通过叠代搜寻最优值。但是并没有遗传算法用的交叉(crossover)以及变异(mutation)。而是粒子在解空间追随最优的粒子进行搜索。 同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域。 -Particle swarm optimization (PSO) is an evolutionary computing (evolutionary computation), there is invented by Dr. Eberhart and Dr. kennedy. From the behavior of birds of prey. PSO with genetic algorithm is similar to an iteration-based optimization tool. System is initialized to a group of random solutions, the optimal value by iterative search. But there is no genetic algorithm with the cross (crossover) and mutation (mutation). But the particles in the solution space to follow the optimal particle search. Comparison with genetic algorithms, PSO has the advantage of simple and easy to implement and there is no need to adjust many parameters. Has been widely used in function optimization, neural network training, fuzzy system control, and other genetic algorithm applications.
Platform: | Size: 10240 | Author: 天涯 | Hits:

[matlab] PSO

Description: 粒子群算法滤波器，用粒子群算法实现了LP滤波器-Particle swarm algorithm filter, particle swarm algorithm with the LP filter
Platform: | Size: 3072 | Author: willago11 | Hits:

[matlab] GA-and-PSO

Description: GA遗传算法与PSO离子群组合算法matlab程序，以下是使用本程序的简单介绍与使用步骤 1）修改设计变量个数 NPar变量定义的是设计变量个数，本例为8，使用时需根据自己实际情况进行修改。 2）修改设计变量的上下限 VarLow 与VarHign定义的是设计变量的上下限，使用时需根据自己实际情况进行修改。记住，变量的维数，要与1）变量个数一致哦。 3）修改FunName 变量FunName定义的是优化目标函数值的计算函数，根据自己实际情况改成自己的函数名。 4）修改最大迭代次数MaxIterations 要根据自己的问题实际，通过试算找出合适的MaxIterations数。 如果，你对GA与PSO比较精通，还可以通过修改KeepPercent、CrossPercent来提高算法的效率，但是对于初学者来说，上述的步骤与操作已经足够，所以其它不再赘述。祝你好运！ -GA genetic algorithm and PSO Ion combination algorithm matlab program, the following is a brief description of the use of this procedure with the use of step a) to modify the design variables are defined by the number of NPar variable number of design variables, in this case eight, when used according to need their actual situation changes. 2) modify the design variables on the lower limit is defined VarLow and VarHign upper and lower limits of design variables need to be modified when used according to their actual situation. Remember, dimensions variable, the number to be with a) variable consistency oh. 3) Modify FunName variable FunName definition is to optimize the objective function value calculation function, according to their actual situation into their own function name. 4) Modify the maximum number of iterations MaxIterations issue according to their actual, through spreadsheets to find the appropriate number MaxIterations. If you are more proficient GA and PSO, but also can
Platform: | Size: 123904 | Author: 天天 | Hits:

[Software Engineering] GA-PSO

Description: 粒子群算法与遗传算法的联合的GA-PSO算法运用，带有测试函数-Joint GA-PSO algorithm using particle swarm optimization and genetic algorithm with test function
Platform: | Size: 12288 | Author: 张煜坤 | Hits:

[matlab] PSO算法程序

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.)
Platform: | Size: 10240 | Author: lazyxiaoming | Hits:

[matlab] GA-PSO

Description: 本算法为用遗传算法改进粒子群GA-PSO算法，附带含有程序使用说明。(This algorithm uses genetic algorithm to improve particle swarm optimization GA-PSO algorithm, with instructions for the use of the program.)
Platform: | Size: 122880 | Author: 1/2 | Hits:

[matlab] 改进型pso算法

Description: 该算法中将速度位移公式与遗传算法相结合用于结果解决多配送中心的路径优化问题(In this algorithm, the velocity displacement formula is combined with the genetic algorithm to solve the path optimization problem of multiple distribution centers)
Platform: | Size: 2048 | Author: 森林梦幻 | Hits:
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