Search - speed particle swarm optimization

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[Mathimatics-Numerical algorithms] PSO DL : 0: 本演示程序模拟了在二维面板上随机排布的任意数量的微粒,向目标聚集的过程。蓝色微粒构成一个微粒群；群落中的个体通过对自身的认知和与其他个体的交互判断飞行的速度和方向，逐步靠近目标。这是一个典型的微粒群优化过程。-This demo program to simulate the random arrangement of any number of particles in the two-dimensional panel, the aggregation process to the target. The blue particles form a particle swarm individuals in the community through their own awareness and interaction with other individuals to judge the speed and direction of flight, and gradually close to the target. This is a typical particle swarm optimization process.
Date : 2026-01-07 Size : 18kb User : 陈晓川
[Mathimatics-Numerical algorithms] PSO_0.3-1.bin DL : 0: This document introduces the Particle Swarm Optimization (PSO) in Scilab. The PSO method, published by Kennedy and Eberhart in 1995, is based on a population of points at first stochastically deployed on a search field. Each member of this particle swarm could be a solution of the optimization problem. This swarm flies in the search field (of D dimensions) and each member of it is attracted by its personal best solution and by the best solution of its neighbours. Each particle has a memory storing all data relating to its flight (location, speed and its personal best solution). It can also inform its neighbours, i.e. communicate its speed and position. This ability is known as socialisation. For each iteration, the objective function is evaluated for every member of the swarm. Then the leader of the whole swarm can be determined: it is the particle with the best personal solution. The process leads at the end to the best global solution.
Date : 2026-01-07 Size : 135kb User : ahmad
[Mathimatics-Numerical algorithms] 13 DL : 0: 粒子群算法（PSO）是一种基于群体的随机优化技术。与其它基于群体的进化算法相比，它们均初始化为一组随机解，通过迭代搜寻最优解。不同的是：进化计算遵循适者生存原则，而PSO模拟社会。将每个可能产生的解表述为群中的一个微粒，每个微粒都具有自己的位置向量和速度向量，以及一个由目标函数决定的适应度。所有微粒在搜索空间中以一定速度飞行，通过追随当前搜索到的最优值来寻找全局最优值。 PSO模拟社会采用了以下三条简单规则对粒子个体进行操作：①飞离最近的个体，以避免碰撞。②飞向目标。③飞向群体的中心。这是粒子群算法的基本概念之一。粒子群算法其基本思想是受许多鸟类的群体行为进行建模与仿真研究结果的启发-Particle swarm optimization (PSO) is a population based stochastic optimization techniques. Based on evolutionary algorithms compared with other groups, they are initialized to a random solution, iterative search through optimal solution. The difference is: the principle of survival of the fittest evolutionary computation to follow, while PSO simulation community. The potential of each solution expressed as a group of particles, each particle has its own position vector and the velocity vector, and a fitness determined by the objective function. All particles in the search space at a constant speed flight, by following the current search to find the optimal values of the global optimum. PSO simulation community has adopted the following three simple rules for the operation of individual particles: ① recently departed individuals, in order to avoid collisions. ② to the target. ③ fly to center groups. This is one of the basic concepts of particle swarm algorithm. PSO algori
Date : 2026-01-07 Size : 6kb User : hhhh
[Mathimatics-Numerical algorithms] IQPSO DL : 0: 量子微粒群算法，用于参数寻优，收敛速度较快。(Quantum particle swarm optimization (QPSO) algorithm is used to optimize parameters with fast convergence speed.)
Date : 2026-01-07 Size : 1kb User : lvzhongyuan