基于VB和Access的温度采集系统的设计与实现

针对函数优化问题,提出了一种基于离差平方和法的粒子群优化算法。该算法用混沌序列初始化粒子的位置和速度,选择好于粒子群优化算法产生的粒子位置。通过离差平方和法进行聚类,利用分类方式来更新粒子的速度。最后将算法应用到3个典型的函数优化问题中,数值结果比较表明,提高了算法搜索能力,全局最优解的精度和收敛速度。

A particle swarm optimization algorithm based on WARD

A particle swarm optimization algorithm with chaos base on WARD （CPSOW） is proposed. In this algorithm, The chaotic sequence is used for producing the position and the speed for every particle. Through the particle＇s position compare with the position that particle swarm optimization algorithm has produced, chooses the preferable position for every particle. At the same time WARD method is introduced in order to enhance the algorithm to search ability, Use classification way the speed of the update particles in the CPSOW algorithm. Finally the CPSOW algorithm is applied to three typical function optimization problem and is comparatively analysed with other variant particle swarm optimization algorithm. The numerical results show that the CPSOW algorithm improves the capacity of Local searching optimal solution,computational precision of global solution and convergence speed.