一类新颖的粒子群优化算法

A Novel Particle Swarm Optimization Algorithm

粒子群优化(PSO)是一类有效的随机全局优化技术。它利用一个粒子群搜索解空间,每个粒子表示一个被优化问题的解,通过粒子间的相互作用发现复杂搜索空间中的最优区域。提出一类新颖的PSO算法,该算法在基本PSO算法的粒子位置更新公式中增加了一个积分控制项。积分控制项根据每个粒子的适应值决定粒子位置的变化,改善了PSO算法摆脱局部极小点的能力。另外,该算法增加了限制搜索空间范围的机制,这对某些函数优化问题是必需的。用5个基准函数做的对比实验结果显示,该算法优于基本PSO算法以及自适应修改惯性因子的PSO算法。

Particle swarm optimization(PSO)is an efficient stochastic global optimization technique making use of a particle population,where each particle represents a solution to the problem being optimized.The particle swarm algorithms find optimal regions of complex search spaces through the interaction of individuals in a population of particles.A novel variant of original PSO algorithm is proposed in this paper.An item of integral control and a constraint of position are added to the traditional position update rule in order to improve performance of original PSO algorithm.The experiment results demonstrate that proposed algorithm is superior to original PSO algorithm and modified PSO algorithm with inertia weight reduced linearly.