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rnd_particle_algorithm
- 随机粒子群算法对解答NP问题、复杂问题，具有传统算法难以比拟的优势——复杂性低，求得的解具有比较理想——当然，对待不同的问题，我们还需要进一步进行问题领域的编码！
goplotpso
- 改进全局粒子群算法,加入时下论文中介绍的收敛因子和惯性权重，比传统基本算法效率高-Particle swarm optimization to improve the overall situation, adding the current paper are described in the convergence factor and inertia weight, the basic algorithm than the traditional efficient
PathPlanningforMobileRobotsBasedontheNeuralNetwork
- ：针对移动机器人传统路径规划算法效率不高，寻优能力差等问题，提出一种基 于神经网络和粒子群优化算法相结合的移动机器人路径规划方法．该方法利用神经网 络实现大量的并行和分布计算，发挥PSO简单、容易实现的优点，提高了路径规划的计 算效率和可靠性．仿真结果表明，这种新路径规划方法是可行且有效的．-The quality and eficiency of calculation is the two puzzling problems in the tradi— tional algo
wfd
- 粒子群算法的研究 希望与大家共同进步 粒子群是一种新型的算法 与传统的遗传算法有一些不同-Particle swarm optimization research is to progress together with you is a new particle swarm algorithm and traditional genetic algorithms have a number of different
psonn
- 粒子群算法优化神经网络,神经网络为传统的多层感知器。-pso optimizes neural networks. the neural networks are multilayer percetrons.
PSOBC-dvhop
- 基于改进的粒子群算法的一个网络节点定位算法，并和传统的DVhop方法做了比较。-Based on the improved particle swarm algorithm of a network node localization algorithm, and the method and traditional DVhop were compared.
mapso
- 本程序用matlab编写，在传统的基本粒子群算法基础上结合多智能体思想，编写了基于多智能体的粒子群算法，并结合电力负荷分配问题比较了其实际效果-This program with matlab, in the traditional basic particle swarm algorithm combining based on multi-agent thought, write based on multi-agent particle swarm algorithm, and combi
constrain-opt
- 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
PSO
- 针对传统的算法如遗传算法、粒子群算法等在TSP问题上求解精确性和求解规模上都还有一定的不足，本文提出了一种基于动态规划思想的粒子群优化算法。该算法用动态规划的方法实现粒子间的信息交互和粒子的进化，并且将粒子群中的粒子按无标度信息指导网络拓扑图的方式进行连接。仿真结果表明该方法能有效地减小误差率，提高解的精确，同时还保持了较低的计算复杂度，具有良好的稳健性。-TSP problem solving for the traditional algorithms such as genetic alg
New-chaos-particle--algorithm
- 针对传统粒子群算法初期收敛较快，而在后期容易陷入早熟、局部最优的特点，提出了一种新的混沌粒子群优化算法-For early traditional PSO algorithm converges faster and easier in the late fall early, optimal local characteristics, we propose a new chaotic particle swarm optimization
PSOaGA
- 粒子群算法和遗传算法分别求解函数的最大值，并在传统的粒子群和遗传算法基础上做了改进，大大提高了搜索的效率-PSO and genetic algorithms to solve the maximum function, respectively, and in the traditional basis of particle swarm and genetic algorithms has been improved, greatly improving the efficiency of
PSOBP
- 包含两个部分：1传统粒子群算法优化BP神经网络 2改进的粒子群算法优化BP神经网络源代码-It consists of two parts: a traditional particle swarm optimization BP neural network 2 Improved particle swarm optimization BP neural network source code
cuckoo_search.m
- Xin she Yang提出的布谷鸟算法，用来解决最优化问题。相比于传统的遗传算法和粒子群算法，该算法的效率更高。-It is published by Xin she Yang and is able to solve the optimization question。it is one of the heuristic algorithm and has the advantage compared with the GA and PSO.
Events-new
- 对于传统粒子群算法的改进，加入了均值删除粒子和惯性权重和收敛速度等限制，有效提高了运行速度-the newly pso method of finding the maximum point,let the rate of process rapidly
PSOBP
- 传统粒子群算法优化BP神经网络和改进的粒子群算法优化BP神经网络源代码-traditional particle swarm optimization BP neural network 2 Improved particle swarm optimization BP neural network source code
PSO-BP
- 粒子群算法优化BP神经网络，在传统PSO算法的基础上增加了惯性权重，并且线性递减策略改变。(Particle Swarm Optimization (BP) algorithm optimizes BP neural network, and inertia weights are added based on traditional PSO algorithm, and linear decreasing strategy changes)
PSO_immu
- 更好解决NP-hard问题，智能启发式算法，降低了传统穷举法的时间复杂度，在解决优化问题方面有突出表现。程序带有大量注释可供快速学习入门。(In order to solve the NP-hard problem better, intelligent heuristic algorithm reduces the time complexity of the traditional exhaustive method, and has outstanding performance in s
nichingparticle-swarm-optimization
- 粒子群优化算起源于对鸟群、鱼群以及对某些社会行为的模拟，是一种基于群体智能的进化计算技术。而小生境技术则起源于遗传算法，这种方法能使基于群体的随机优化算法形成物种，从而使相应的优化算法具有发现多个最优解的能力。而多分类器集成技术则是通过多个分类器进行某种组合来决定最终的分类，以取得比单个分类器更好的性能。多分类器集成技术要求基元分类器不仅个体性能要好并且其差异度要大，这与小生境技术形成物种的能力具有很多内在的相似性。目前己经有研究者将小生境技术应用于多分类器集成，但由于传统的小生境技术仍然不完善
程序
- 粒子群算法优化的BP神经网络与传统BP神经网络对比(Comparison between BP neural network optimized by particle swarm optimization and traditional BP neural network)
粒子群算法优化pid源码 matlab仿真
- 粒子群算法（PSO）整定pid控制参数，比传统Z-N整定方法要好，内附matlab程序与simulink模型(Particle swarm optimization (PSO) is better than traditional Z-N tuning method in tuning PID control parameters. It includes matlab program and Simulink model.)