摘要
粒子群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,提出了一种新的粒子群算法,算法对粒子的速度和位置更新公式进行了改进,使粒子在其最优位置的基础上进行位置更新,增强了算法的寻优能力.通过对5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,a new particle swarm optimization which improves particle＇s velocity and position update rule to adjust its movement based on the individual best position is proposed in the paper.The modified algorithm can enhance capability of optimization.Five benchmark functions are tested,and the results indicate that the modified particle swarm optimization is effective to find the global optimal solution.
出处
《哈尔滨理工大学学报》
CAS
北大核心
2010年第2期31-34,共4页
Journal of Harbin University of Science and Technology
关键词
粒子群算法
群体智能
进化计算
particle swarm optimization
swarm intelligence
evolutionary computation
