电脑与电信
電腦與電信
전뇌여전신
COMPUTER & TELECOM
2012年
6期
34-35
,共2页
粒子群算法%合作算子%群智能
粒子群算法%閤作算子%群智能
입자군산법%합작산자%군지능
particle swarm optimization algorithm%cooperation operator%swarm intelligence
为了解决粒子群优化算法容易陷入局部最优和后期搜索精度不高的问题，提出了带有合作算子的改进粒子群算法。合作算子和粒子运动公式的动态调整改善种群的多样性并且提高了搜索精度。从算法的收敛性、准确性和稳定性等方面对这种改进算法进行分析和实验，发现均优于标准粒子群优化（PSO）算法。
為瞭解決粒子群優化算法容易陷入跼部最優和後期搜索精度不高的問題，提齣瞭帶有閤作算子的改進粒子群算法。閤作算子和粒子運動公式的動態調整改善種群的多樣性併且提高瞭搜索精度。從算法的收斂性、準確性和穩定性等方麵對這種改進算法進行分析和實驗，髮現均優于標準粒子群優化（PSO）算法。
위료해결입자군우화산법용역함입국부최우화후기수색정도불고적문제，제출료대유합작산자적개진입자군산법。합작산자화입자운동공식적동태조정개선충군적다양성병차제고료수색정도。종산법적수렴성、준학성화은정성등방면대저충개진산법진행분석화실험，발현균우우표준입자군우화（PSO）산법。
To avoid the problem of premature convergence and poor accuracy in later period, an improved particle swarm optimization （IPSO） algorithm with cooperation operator is introduced. Cooperation operator and dynamic adjustment of particle movement formula improve the diversity of the population and algorithm accuracy. Its convergence, accuracy, and stability with two benchmark function are test. It is found that IPSO outperforms the normal PSO algorithm clearly.
