改进布谷鸟算法在边坡滑面搜索中的应用

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英文篇名：Application of Improved Cuckoo Search in Slip Surface Search 作者：杨俊毅 ; 罗磊 英文作者：YANG Junyi;LUO Lei;Yunnan Highway Engineering Supervision and Consultancy Company;South Central University; 关键词：布谷鸟搜索 ; 蒙特卡罗优化 ; 安全系数 ; 退火模拟 ; 可靠度 英文关键词：cuckoo search;;Monte-Carlo optimization;;safety factor;;simulated annealing;;reliability 中文刊名：路基工程 英文刊名：Subgrade Engineering 机构：云南省公路工程监理咨询公司;中南大学; 出版日期：2019-04-20 出版单位：路基工程 年：2019 期：02 语种：中文; 页：166-168 页数：3 CN：51-1414/U ISSN：1003-8825 分类号：TU43;TP18

摘要

根据布谷鸟算法前期搜索随机跳跃性大、后期搜索收敛速度慢的缺点,引入惯性权重并结合局部蒙特卡罗优化,得到改进的布谷鸟算法,其搜索结果与Spencer法、瑞典条分法进行对比可知,不论滑面的位置还是安全系数都很相近。与退火模拟算法对比验证其可靠度,结果表明:改进的布谷鸟算法更优秀,得到的结果更精确,可以应用于边坡滑面搜索。
        Because cuckoo search algorithm has defects, presenting random big leap in prophase search and slow convergence in late search, inertia weight combined with local Monte-Carlo optimization was introduced to improve cuckoo search. The search results, no matter the sliding surface position or the safety factor, were very close to those from Spencer method or Sweden slices method. Furthermore, its reliability was verified by simulated annealing algorithm. The result shows that the improved CS algorithm is better with more accurate solution, thus can be applied to search slip surface of slope.

引文

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