A Combinatorial Search Method Based on Harmony Search Algorithm and Particle Swarm Optimization in Slope Stability Analysis

2009 International Conference on Computational Intelligence and Software Engineering Pub Date : 2009-12-28 DOI:10.1109/CISE.2009.5362716

Liang Li, Shibao Lu, Xuesong Chu, Guangming Yu

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引用次数: 6

Abstract

The parameters used in harmony search algorithm and particle swarm optimization are found to be of importance to the results, however, there are no theoretical bases or formulae to determine the values of the parameters. A combinatorial method is proposed which combing the harmony search procedure and the particle swarm optimization for the determination of critical slip surfaces of soil slopes. The individuals in the harmony memory are divided into two equal groups, one of which is used to perform the particle swarm optimization algorithm, and the other is adopted in the generation of new harmony in harmony search algorithm, the optimums found by these two groups are compared and communal learning mechanism is formed. In addition, the dynamic adaptation strategy for the determination of values of parameters in these two algorithms is proposed. This combinatorial search algorithm is demonstrated to be efficient and effective for the slope stability analysis. KeywordsArtificial intelligence; Harmony search algorithm; Particle swarm optimization; Slope stability analysis

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基于和谐搜索算法和粒子群优化的边坡稳定性分析组合搜索方法

和声搜索算法和粒子群优化中使用的参数对结果有重要影响，但没有理论依据和公式来确定参数的取值。提出了一种结合和谐搜索法和粒子群优化法确定土质边坡临界滑动面的组合方法。将和声记忆中的个体平等分成两组，其中一组用于执行粒子群优化算法，另一组用于和声搜索算法中新和声的生成，比较两组找到的最优，形成公共学习机制。此外，提出了两种算法中参数取值的动态自适应策略。结果表明，该组合搜索算法在边坡稳定性分析中是有效的。KeywordsArtificial情报;和谐搜索算法;粒子群优化;边坡稳定性分析

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2009 International Conference on Computational Intelligence and Software Engineering

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