摘要
粒子群优化算法是求解函数优化问题的一种新的进化算法,然而它在求解高维函数时容易陷入局部最优.为了克服这个缺点,通过调整粒子的速度更新公式,使粒子获得更多信息来调整自身的状态,以增强算法跳出局部最优的能力.通过对6个基准函数的仿真实验,表明了改进算法的有效性.
Particle swarm optimization is a new computational method for tackling optimization functions. However, it is easily trapped into the local optimization when solving high-dimension functions. To overcome this shortcoming, by changing particle＇s velocity update rule to help the particle acquire more information of others to adjust its movement. The modified algorithm can improve the ability of seeking the global excellent result. Six benchmark functions are tested, and the result indicates that the modified particle swarm optimization is effective to find the global optimal solution.
出处
《哈尔滨商业大学学报（自然科学版）》
CAS
2009年第4期464-466,共3页
Journal of Harbin University of Commerce：Natural Sciences Edition
关键词
粒子群优化算法
群体智能
进化计算
particle swarm optimization
swarm intelligence
evolutionary computation
