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土坡极限平衡稳定分析中临界滑动面的搜索是一个复杂的优化问题,在应用常规微粒群算法搜索时往往因参数较多且难以确定以及飞行速度越界的缺陷而陷入局部最优。基于对常规微粒群算法寻优思想的分析,借鉴和声算法的搜索策略来更新粒子的位置,提出基于和声策略的微粒群优化算法,该方法继承了常规微粒群算法中利用本身经验和社会认知的优势,又借鉴了和声策略的简单易行优势。将该方法应用于土坡稳定分析中,通过算例比较分析,证明新算法的有效性。
The search of critical slip surface in the limit equilibrium stability analysis of earth slope is a complex optimization problem. When the conventional particle swarm optimization algorithm is applied to search, it often gets trapped in local optimum due to the more parameters and the difficulty of determining and the crossing of the flying speed. Based on the analysis of optimization idea of conventional particle swarm optimization algorithm and the search strategy of harmony algorithm, the particle swarm optimization algorithm is proposed based on the harmony algorithm. This method inherits the advantages of the conventional particle swarm algorithm Cognitive advantage, but also draws on the harmony strategy simple and easy to use advantages. The method is applied to the stability analysis of soil slopes, and the validity of the new algorithm is proved through comparative analysis.