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 Opt

程序提出了一种基于人群混合算法，是粒子群优化和引力搜索算法的组合。主要目标提高整合PSO和GSA算法的能力-Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA) A new hybrid population-based algorithm (PSOGSA) is proposed with the combination of Particle Swarm Optimization

In computer science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. PSO optimizes a problem by having a population

一种带压缩因子的pso算法,能够处理无约束优化问题。-With one kind of compression algorithm pso factor, able to handle unconstrained optimization problems

In computer science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of c