دوره 6، شماره 4 - ( 7-1395 )                   جلد 6 شماره 4 صفحات 567-578 | برگشت به فهرست نسخه ها


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Shahrouzi M, Rashidi Moghadam M. GROUND MOTION CLUSTERING BY A HYBRID K-MEANS AND COLLIDING BODIES OPTIMIZATION. International Journal of Optimization in Civil Engineering. 2016; 6 (4) :567-578
URL: http://ijoce.iust.ac.ir/article-1-273-fa.html
GROUND MOTION CLUSTERING BY A HYBRID K-MEANS AND COLLIDING BODIES OPTIMIZATION. دانشگاه علم و صنعت ایران. 1395; 6 (4) :567-578

URL: http://ijoce.iust.ac.ir/article-1-273-fa.html


چکیده:   (1524 مشاهده)

Stochastic nature of earthquake has raised a challenge for engineers to choose which record for their analyses. Clustering is offered as a solution for such a data mining problem to automatically distinguish between ground motion records based on similarities in the corresponding seismic attributes. The present work formulates an optimization problem to seek for the best clustering measures. In order to solve this problem, the well-known K-means algorithm and colliding bodies optimization are employed. The latter acts like a parameter-less meta-heuristic while the former provides strong intensification. Consequently, a hybrid algorithm is proposed by combining features of both the algorithms to enhance the search and avoid premature convergence. Numerical simulations show competative performance of the proposed method in the treated example of optimal ground motion clustering; regarding global optimization and quality of final solutions.

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نوع مطالعه: پژوهشي | موضوع مقاله: Optimal design
دریافت: ۱۳۹۵/۲/۷ | پذیرش: ۱۳۹۵/۲/۷ | انتشار: ۱۳۹۵/۲/۷

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