摘要： 为解决粒子群优化(PSO)算法的早熟收敛问题，提出一种群活性反馈PSO进化算法SAF-PSO。利用群活性加速度作为多样性测度，当群活性加速下降时，对粒子的位置和速度分别执行进化和变异操作，增强粒子跳出局部最优的能力，提高寻找全局最优的几率。对基准函数的仿真结果表明，与其他PSO算法相比，该算法具有更强的全局搜索能力和更高的寻优精度。
关键词:
粒子群优化,
群活性,
进化,
变异,
全局搜索
Abstract: Aiming at the premature convergence problem in Particle Swarm Optimization(PSO) algorithm, a new evolutionary PSO algorithm with Swarm Activity Feedback(SAF-PSO) is proposed. The method uses swarm activity as diversity index. When swarm activity is quickened to descend, the evolution or mutation operation are added to the iterative process to modify the positions or velocities of particles in order to increase the ability of algorithm to break away from the local optimum and to find the global optimum is greatly improved. Experimental results on several benchmark functions and the comparison with other algorithms show that SAF-PSO has strong global search ability and high accuracy.
Key words:
Particle Swarm Optimization(PSO),
swarm activity,
evolution,
mutation,
global search
中图分类号:
左旭坤, 苏守宝. 一种群活性反馈粒子群优化算法[J]. 计算机工程, 2012, 38(13): 182-184.
ZUO Xu-Kun, SU Shou-Bao. Particle Swarm Optimization Algorithm with Swarm Activity Feedback[J]. Computer Engineering, 2012, 38(13): 182-184.