摘要
针对一般粒子群算法收敛速度慢易发散的缺点,提出一种速度改进型粒子群优化算法。该算法对速度的最大值进行动态改变,可以使粒子群算法在前期保持快速而又全局范围的探测搜索,而在后期,也可以将粒子限定在局部的重点区域的探测搜索。采用速度改进型粒子群优化算法对典型的多峰函数进行优化,仿真结果表明方法的有效性,并通过与一般粒子群算法进行比较,表明方法能够加快粒子群算法的收敛速度,具有更好的优化性能。
Proposes a speed improved particle swarm optimization in order to improve the convergent speed of common particle swarm optimization. The algorithm dynamically changes the maximum speed, and maintains a rapid and global scope search at early, the particle can also be limited to the key areas in the latter. The algorithm proposed is used to optimize the typical multimodal function. The simulation result shows the validity of the algorithm. The method proposed is compared with the common algorithms. The result shows that the new method can improve the convergent speed of the algorithm and has a better performance.
出处
《现代计算机》
2011年第19期3-6,共4页
Modern Computer
关键词
粒子群优化算法
速度优化
多峰函数
Particle Swarm Optimization
Speed Optimization
Multimodal Function
