Search - speed particle swarm optimization

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[AI-NN-PR] PSO-C DL : 0: 在C语言环境下使用的粒子群优化算法，需要给出最大速度、迭代次数、作为中断条件的最小误差等初始条件。-in the C-language environment to the use of the PSO algorithm, the greatest need is speed, the number of iteration, as the smallest disruption error conditions such as initial conditions.
Date : 2026-01-08 Size : 57kb User :
[AI-NN-PR] psot DL : 1: 粒子群算法工具箱该工具箱将PSO算法的核心部分封装起来，提供给用户的为算法的可调参数，用户只需要定义好自己需要优化的函数(计算最小值或者最大值)，并设置好函数自变量的取值范围、每步迭代允许的最大变化量(称为最大速度，Max_V)等，即可自行优化。-Particle Swarm Optimization Toolbox of the Toolkit will be the core of the PSO algorithm package, and made available to the user adjustable parameters for the algorithm, users only need to define their need to optimize the function (calculation of the minimum or maximum), and set good function from the range of variables, each iteration step the maximum allowable variation (known as maximum speed, Max_V) and so on, can self-optimize.
Date : 2026-01-08 Size : 801kb User : 张鹤峰
[AI-NN-PR] cpso DL : 0: 粒子群优化算法容易理解，实现简单，优化速度快，收敛性强。常用于解决种类最优化问题。-Particle swarm optimization algorithm easy to understand, easy to achieve, and optimize the speed and strong convergence. Types commonly used in the optimization problem to solve.
Date : 2026-01-08 Size : 1kb User : lxd
[AI-NN-PR] mpso1 DL : 0: 这是一个经过改进的PSO算法，是用FORTRAN语言编写的，因为此语言计算速度快，适合PSO的应用，收敛速度明显加快，并且这是改优化后的PSO程序，比标准的速度快。-This is an improved PSO algorithm, are used FORTRAN language, because this language computing speed, suitable PSO applications, significantly speeding up the convergence rate, and change This is the optimized PSO procedures, faster than the standard.
Date : 2026-01-08 Size : 1kb User : xin
[AI-NN-PR] pso1 DL : 0: 微粒群算法与其它进化类算法相类似，也采用“群体”与“进化”的概念，同样也是依据个体（微粒）的适应值大小进行操作。所不同的是，微粒群算法不像其它进化算法那样对于个体使用进化算子，而是将每个个体看作是在n维搜索空间中的一个没有重量和体积的微粒，并在搜索空间中以一定的速度飞行。-Particle swarm optimization algorithm with other similar type of evolution, but also the use of " groups" and " evolution" concept is also based on the individual (particle) size of the operation of fitness. The difference is that unlike other particle swarm optimization evolutionary algorithm as the use of evolution for the individual operator, but as each individual in the n-dimensional search space of a weight and size of the particles, and in the search space the speed of a certain flight.
Date : 2026-01-08 Size : 223kb User : sunguili
[AI-NN-PR] Swarm_Optimization_Algorithm_with_C DL : 1: 改进的粒子群算法--自适应粒子群算法，在普通的粒子群算法里面加入了熵和平均粒距的概念，收敛速度大大提高，用C实现-Improved particle swarm- adaptive particle swarm optimization, in which ordinary PSO joined the entropy and the concept of average distance, speed up the convergence, with the C implementation
Date : 2026-01-08 Size : 166kb User : 翁海冰
[AI-NN-PR] bayesianswarm DL : 0: This code expains bayesian particle swarm optimization method.All files have been written on matlab 2007a. This method has been explianed with various benchmark functions. This optimization method can be directly compared with other unconstrained optimization method like GA or pso for effieciency and speed.
Date : 2026-01-08 Size : 4kb User : missed2010
[AI-NN-PR] liziqunpso DL : 0: 粒子群算法是一种进化计算技术，来源于对鸟群捕食的思考,最早由Kenney与Eberhart 于1995年提出。在PSO中，寻找最优解被看做群体寻找目标。个体在搜索的过程中具有自己的位置和搜索速度。个体追寻最优个体在解空间中进行搜索。搜索的过程是一个反复的迭代过程。在这个过程中，个体完成的任务一是找寻自己认可的最优解；另个任务是获知群体得到的暂时最优解。 -The particle swarm optimization is an evolutionary computation technique, derived from the thinking of the birds of prey, was first proposed by Kenney and Eberhart in 1995. PSO, to find the optimal solution is seen as a group to find the target.The individual has its own position in the search process and search speed. The individual pursuit of the best individual in the solution space to search. The search process is an iterative process repeated. In this process, individuals complete the task first, find the optimal solution to themselves another task is informed groups of temporary optimal solution.
Date : 2026-01-08 Size : 6kb User : 刘鹏飞
[AI-NN-PR] constrain-opt DL : 0: 针对工程优化设计问题,提出了基于混沌粒子群算法的工程约束优化问题求解方法。CPSO算法利用混沌搜索的全局遍历性、随机性和规律性等特点, 引导粒子在全局范围内搜索, 从而克服了传统粒子群算法早熟收敛的缺点。该算法以种群适应度方差作为粒子群优化算法早熟收敛的判据, 并用惩罚函数法处理违法约束的粒子, 当基本粒子群算法陷入早熟时, 随机选择粒子群中的部分粒子实施混沌搜索, 直至满足迭代收敛条件为止。CPSO算法能提高种群的多样性和粒子搜索的遍历性, 从而有效提高了PSO算法的收敛速度和精度。两个工程约束优化实例的求解结果表明,该算法的优化结果最好, 收敛速度也比较快-Based on engineering design optimization problems, and put forward based on chaotic particle swarm optimization algorithm of engineering problem solving methods. CPSO algorithm by using chaos search The global ergodicity, stochastic characteristics and regularity, and lead particles in the global scope search, and overcome the traditional particle swarm algorithm premature convergence faults. In this algorithm, the population fitness variance as the particle swarm optimization algorithm of the criterion of premature convergence, the penalty function method and deal with illegal constraint particles, when basic particle swarm to calculate Law in early maturity, random selection of particle swarm of these particles implementation chaotic search, until the convergence conditions meet so far. CPSO algorithm can improve the population Sample sex and particles of searching ergodicity, thus effectively improved PSO algorithm convergence speed and accuracy. Two engineering constraint optim
Date : 2026-01-08 Size : 728kb User : 吴胜亮
[AI-NN-PR] Particle-Swarm-Optimization-method DL : 1: 本程序采用基本粒子群算法，共有8个测试函数，测试算法对复杂函数的收敛速度和收敛方差-This program uses a Particle Swarm Optimization, a total of eight test functions, test algorithm convergence speed and variance of complex functions
Date : 2026-01-08 Size : 8kb User : liunan
[AI-NN-PR] Apso-bp-Rainfall DL : 1: 降水短期气候预测是一个非常复杂、重要的研究课题。为了提高其预测能力，拟采用1959—2011 年逐月74 项大气环流特征量序列、月平均500 hPa 高度场和月平均海温场，选取预测因子；用主分量分析方法提取样本数据中主要信息为综合因子。用粒子群优化人工神经网络方法，建立宣城市夏季降水短期气候预测模型。对2007—2011 年宣城市夏季降水预报检验结果表明，粒子群优化人工神经网络收敛速度快，迭代次数少；试报平均绝对误差是66.5 mm，绝对值平均相对误差10.5 ，预测精度高，具有很好的应用推广前景。 -Precipitation of short-term climate prediction is a very complex and important research topic. Intends to adopt in order to improve its ability to predict the the 1959-2011 monthly 74 atmospheric circulation feature series, monthly mean 500 hPa height field and monthly average sea surface temperature field, select the predictor extract the sample data using principal component analysis for the Synthesis factor. Artificial neural network using particle swarm optimization method, Xuancheng City in summer rainfall in short-term climate prediction model. 2007-2011 declared the city in summer precipitation forecast verification results show that the particle swarm optimization artificial neural network convergence speed, fewer iterations trial reported an average absolute error is 66.5 mm, the absolute value of the average relative error of 10.5 , high prediction accuracy, good application prospect.
Date : 2026-01-08 Size : 1.52mb User : mali
[AI-NN-PR] FLO DL : 0: 基于粒子群优化算法的无源模拟滤波器优化设计方法容易陷入局部最优,收敛速度慢迭代次数多、运算量大且稳定性不够好。提出果蝇优化算法对滤波器的整个参数空间进行高效并行搜索直到获得最优的参数值,实例仿真表明,采用该方法设计的滤波器在相同的带宽准确度及阻带衰减的情况下,具有更快的运算速度及收敛性能。-Passive analog filter optimization algorithm based on particle swarm optimization design method is easy to fall into local optimum, the slow convergence iterations, large amount of computation and stability is not good enough. Efficient parallel search proposed Drosophila optimization algorithm for the whole parameter space of the filter until the optimal parameter values, the simulation results show that the designed filter in the case of the same bandwidth accuracy and stop-band attenuation, faster computing speed and convergence performance.
Date : 2026-01-08 Size : 1kb User : tdy
[AI-NN-PR] Opposition-based-Stud-Genet DL : 1: 为进一步提高螺栓遗传算法的优化效率，加速寻优过程，提出基于对立策略的螺栓遗传算法。该算法在种群初始化阶段和变异阶段均用对立取代随机方式，提高产生解的质量。利用测试函数对算法的效率进行检验，将其与差分算法、遗传算法、粒子群算法和螺栓遗传算法进行对比，结果表明，新算法具有更快的收敛速度和更高的求解精度。-In order to improve the performance of Stud Genetic Algorithm(SGA) and accelerate the convergence speed, an improved stud genetic algorithm based on opposition is proposed. Conventional random method is replaced with opposition method in both population initialization and mutation, which can improve the quality of solutions. Based on benchmark functions, the optimization performance of the algorithm is compared with genetic algorithm, different evolutionary, particle swarm optimization and stud genetic algorithm, the results show that the new algorithm has better optimization performance.
Date : 2026-01-08 Size : 95kb User : zhangyan
[AI-NN-PR] PSO_nature DL : 0: 本程序为基于自然选择的粒子群优化算法，注释较为详细。基于自然选择的粒子群优化算法是借鉴自然选择的机理，根据粒子的适应度进行排序，加快算法收敛的速度。-This procedure for the particle swarm optimization algorithm based on natural selection and detailed comments. The particle swarm optimization algorithm based on natural selection is a reference to the mechanism of natural selection, according to the particle s fitness sorting, accelerate the algorithm convergence speed.
Date : 2026-01-08 Size : 1kb User : 邓振立
[AI-NN-PR] PSO-algorithm DL : 0: pso粒子群优化算法，所有的粒子都有一个由被优化的函数决定的适应值，每个粒子还有一个速度决定他们运动的方向和距离，然后粒子就追随当前的最优粒子在解空间中搜索-Pso particle swarm optimization algorithm, all particles have an optimized value determined by the function, each particle also has a speed to determine their movement direction and distance, and then the particles follow the current optimal particle in the solution space search for
Date : 2026-01-08 Size : 743kb User : 胡一萌
[AI-NN-PR] PSO_adaptation DL : 1: 改进型粒子群PSO优化算法MATLAB代码，基于权重改进速度，已封装为函数(Improved particle swarm optimization algorithm PSO MATLAB code, based on the speed of weight improvement, has been encapsulated as a function.)
Date : 2026-01-08 Size : 1kb User : 影韬