新疆石油地质

新疆石油地质 ›› 2011, Vol. 32 ›› Issue (6): 656-659.

• 应用技术 • 上一篇    下一篇

利用遗传-粒子群优化混合算法求取剩余静校正量

何超群1, 王彦春1, 张品2   

  1. 1.中国地质大学地球物理与信息技术学院,北京 100083;
    2.中国石油东方地球物理公司研究院,河北涿州 072751
  • 收稿日期:2011-05-04 出版日期:2011-12-01 发布日期:2020-08-20
  • 作者简介:何超群（1979-）,男,湖北仙桃人,博士,地球探测与信息技术（Tel）15101155817（E-mail）hocq@sohu.com.

Application of PSO-GA Hybrid Algorithm to Residual Statics Correction

HE Chao-qun1, WANG Yan-chun1, ZHANG Pin2   

  1. 1. School of Geophysics and Information Technology, China University of Geosciences, Beijing 100083, China;
    2. Research Institute, BGP Inc., CNPC, Zhuozhou, Hebei 072751, China
  • Received:2011-05-04 Online:2011-12-01 Published:2020-08-20

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摘要： 剩余静校正是一个具有多参数、多极值的全局优化问题,当大量未知参数存在时,常规的遗传算法（GA）几乎无法避免早熟现象,难以保证收敛于全局最优。结合粒子群优化算法（PSO）和遗传算法的优势,提出了一种新颖的粒子群优化-遗传混合算法。混合算法利用了粒子群优化算法的速度和位置的更新规则,并引入遗传算法里的交叉变异思想。用混合算法和遗传算法分别对两个理论模型进行试处理,处理结果表明,混合算法比遗传算法具有更好的性能,是一种求取复杂地形条件下剩余静校正量的实用方法。

关键词: 剩余静校正, 遗传算法, 粒子群算法, 粒子群优化-遗传混合算法

Abstract: The residual statics correction is a global optimization problem with multi-parameter and multi- extreme values. The ordinary genetic algorithm (GA) is almost impossible to avoid the premature phenomenon because of the large number of unknown parameters, so it is difficult to convergence to a global optimum. This paper presents a PSO (particle swarm optimization)-GA hybrid algorithm (PGHA) based on the advantage of PSO and GA. PGHA combines the rules of updating the velocity and situation of PSO with the idea of crossover and mutation of GA. Using PGHA and GA to experimentally deal with these two theoretical models shows that PGHA is better than GA in performance and adaptability and it is a practical method for calculating residual statics correction in complicated topographic conditions.

Key words: residual static correction, genetic algorithm, particle swarm optimization, PSO-GA hybrid algorithm

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