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Structural Engineering and Mechanics
  Volume 45, Number 1, January10 2013, pages 111-127

Efficient gravitational search algorithm for optimum design of retaining walls
Mohammad Khajehzadeh, Mohd Raihan Taha and Mahdiyeh Eslami

    In this paper, a new version of gravitational search algorithm based on opposition-based learning (OBGSA) is introduced and applied for optimum design of reinforced concrete retaining walls. The new algorithm employs the opposition-based learning concept to generate initial population and updating agents\' position during the optimization process. This algorithm is applied to minimize three objective functions include weight, cost and CO2 emissions of retaining structure subjected to geotechnical and structural requirements. The optimization problem involves five geometric variables and three variables for reinforcement setups. The performance comparison of the new OBGSA and classical GSA algorithms on a suite of five well-known benchmark functions illustrate a faster convergence speed and better search ability of OBGSA for numerical optimization. In addition, the reliability and efficiency of the proposed algorithm for optimization of retaining structures are investigated by considering two design examples of retaining walls. The numerical experiments demonstrate that the new algorithm has high viability, accuracy and stability and significantly outperforms the original algorithm and some other methods in the literature.
Key Words
    retaining wall; minimum weight; minimum cost; minimum CO2 emissions; gravitational search algorithm
Mohammad Khajehzadeh, Mohd Raihan Taha: Department of Civil and Structural Engineering, National University of Malaysia, Bangi, Selangor, Malaysia
Mahdiyeh Eslami: Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Kerman, Iran

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