包含：基本粒子群算法、带压缩因子、线性递减权重、自适应权重、随机权重、同步变化、二阶粒子群、混沌粒子群、基于模拟退火的粒子群算法等

遗传算法、神经网络、模拟退火、蚁群算法、模糊逻辑、粒子群优化算法的C语言编程

图像分割是目标识别的首要和关键步骤。目前的图像分割方法有多种, 其中阈值方法优点比较突出, 但是采用阈值方法分 割的关键是要能高效率地找到被分图像的最佳熵阈值。针对这一问题, 将Geese- LDW- PSO 算法的位置更新公式作了改进, 即用 当前种群的全局极值取代所有粒子的当前位置, 并将之用于熵阈值图像分割中。仿真实验表明, 该算法可以快速稳定地获得一幅 图像的最佳分割阈值。仿真结果显示, 该方法对车牌分割具有较好的性能。 专业论文，为广大做毕设同学提供资源

为了让广大学者更直观的了解例子群算法，作者编制了粒子群算法演示程序，能够直观的观察例子群算法的寻优过程，并提供了源程序，供广大学者学习交流。

本程序利用当前流行的pso算法对rbf神经网络进行优化，使之预测精度高

http://www.mathworks.com/matlabcentral/fileexchange/25986-constrained-particle-swarm-optimization Description Previously titled "Another Particle Swarm Toolbox" Introduction Particle swarm optimization (PSO) is a derivative-free global optimum solver. It is inspired by the surprisingly organized behaviour of large groups of simple animals, such as flocks of birds, schools of fish, or swarms of locusts. The individual creatures, or "particles", in this algorithm are primitive, knowing only four simple things: 1 & 2) their own current location in the search space and fitness value, 3) their previous personal best location, and 4) the overall best location found by all the particles in the "swarm". There are no gradients or Hessians to calculate. Each particle continually adjusts its speed and trajectory in the search space based on this information, moving closer towards the global optimum with each iteration. As seen in nature, this computational swarm displays a remarkable level of coherence and coordination despite the simplicity of its individual particles. Ease of Use If you are already using the Genetic Algorithm (GA) included with MATLAB's Global Optimization Toolbox, then this PSO toolbox will save you a great deal of time. It can be called from the MATLAB command line using the same syntax as the GA, with some additional options specific to PSO. This will allow a high degree of code re-usability between the PSO toolbox and the GA toolbox. Certain GA-specific parameters such as cross-over and mutation functions will obviously not be applicable to the PSO algorithm. However, many of the commonly used options for the Genetic Algorithm Toolbox may be used interchangeably with PSO since they are both iterative population-based solvers. See >> help pso (from the ./psopt directory) for more details. Features * NEW: support for distributed computing using MATLAB's parallel computing toolbox. * Full support for bounded, linear, and nonlinear constraints. *

粒子群算法解决背包问题的MATLAB程序

用PSO算法优化Schaffer's f6函数，该函数的全局最小值为y=0，而最优解为（0,0）。

粒子群优化算法 随机摄动 惯性系数 压缩因子 C++实现

粒子群优化算法对BPNN进行超参数优化的python代码实现，亲测可用