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

2009 ◽  
Author(s):  
Liang Li ◽  
Shi-bao Lu ◽  
Xue-song Chu ◽  
Guang-ming Yu
Keyword(s):  
Particle Swarm Optimization ◽  
Stability Analysis ◽  
Slope Stability ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Slope Stability Analysis ◽  
Search Method ◽  
Combinatorial Search ◽  
Swarm Optimization

scholarly journals Application of Harmony Search Algorithm to Slope Stability Analysis

Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1250
Author(s):  
Sina Shaffiee Haghshenas ◽  
Sami Shaffiee Haghshenas ◽  
Zong Woo Geem ◽  
Tae-Hyung Kim ◽  
Reza Mikaeil ◽  
...  
Keyword(s):  
Stability Analysis ◽  
Slope Stability ◽  
Safety Factor ◽  
Search Algorithm ◽  
Limit Equilibrium ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Slope Stability Analysis ◽  
Unit Weight ◽  
Slope Safety

Slope stability analysis is undoubtedly one of the most complex problems in geotechnical engineering and its study plays a paramount role in mitigating the risk associated with the occurrence of a landslide. This problem is commonly tackled by using limit equilibrium methods or advanced numerical techniques to assess the slope safety factor or, sometimes, even the displacement field of the slope. In this study, as an alternative approach, an attempt to assess the stability condition of homogeneous slopes was made using a machine learning (ML) technique. Specifically, a meta-heuristic algorithm (Harmony Search (HS) algorithm) and K-means algorithm were employed to perform a clustering analysis by considering two different classes, depending on whether a slope was unstable or stable. To achieve the purpose of this study, a database made up of 19 case studies with 6 model inputs including unit weight, intercept cohesion, angle of shearing resistance, slope angle, slope height and pore pressure ratio and one output (i.e., the slope safety factor) was established. Referring to this database, 17 out of 19 slopes were categorized correctly. Moreover, the obtained results showed that, referring to the considered database, the intercept cohesion was the most significant parameter in defining the class of each slope, whereas the unit weight had the smallest influence. Finally, the obtained results showed that the Harmony Search algorithm is an efficient approach for training K-means algorithms.

scholarly journals The Buttressed Walls Problem: An Application of a Hybrid Clustering Particle Swarm Optimization Algorithm

Mathematics ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 862 ◽  
Author(s):  
José García ◽  
José V. Martí ◽  
Víctor Yepes
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Particle Swarm ◽  
Retaining Walls ◽  
Harmony Search Algorithm ◽  
Swarm Optimization ◽  
Practical Applications ◽  
Reinforced Earth

The design of reinforced earth retaining walls is a combinatorial optimization problem of interest due to practical applications regarding the cost savings involved in the design and the optimization in the amount of CO 2 emissions generated in its construction. On the other hand, this problem presents important challenges in computational complexity since it involves 32 design variables; therefore we have in the order of 10 20 possible combinations. In this article, we propose a hybrid algorithm in which the particle swarm optimization method is integrated that solves optimization problems in continuous spaces with the db-scan clustering technique, with the aim of addressing the combinatorial problem of the design of reinforced earth retaining walls. This algorithm optimizes two objective functions: the carbon emissions embedded and the economic cost of reinforced concrete walls. To assess the contribution of the db-scan operator in the optimization process, a random operator was designed. The best solutions, the averages, and the interquartile ranges of the obtained distributions are compared. The db-scan algorithm was then compared with a hybrid version that uses k-means as the discretization method and with a discrete implementation of the harmony search algorithm. The results indicate that the db-scan operator significantly improves the quality of the solutions and that the proposed metaheuristic shows competitive results with respect to the harmony search algorithm.

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