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A Novel Multiobjective Optimization for Cement Stabilized Soft Soil based on Artificial Bee Colony
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Author(s): Rahul Khandelwal (Qualcomm Inc., San Diego, CA, USA), J. Senthilnath (Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India), S. N. Omkar (Department of Aerospace Engineering, Indian Institute of Science, Bangalore, India)and Narendra Shivanath (SMEC India, Bangalore, India)
Copyright: 2016
Volume: 7
Issue: 4
Pages: 17
Source title:
International Journal of Applied Metaheuristic Computing (IJAMC)
Editor(s)-in-Chief: Peng-Yeng Yin (Ming Chuan University, Taiwan)
DOI: 10.4018/IJAMC.2016100101
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Abstract
Cement is the most widely used additive in soft soil stabilization due to its high strength and availability. The cement content and curing time have a direct influence on the stabilization cost and hence it is prudent to minimize these variables to achieve optimality. Thus, it is a classical multi-objective optimization problem to find the optimum combination of cement content used and the curing time provided to achieve the target strength. This paper brings out the use of Vector Evaluated Artificial Bee Colony (VEABC) algorithm, a multi-objective variant of Artificial Bee Colony (ABC) technique, for the problem on hand. VEABC is a swarm intelligence algorithm, which employs multiple swarms to handle the multiple objectives and the information migration between these swarms ensures a global optimum solution is reached. Due to the stochastic nature of ABC algorithm, the resulting Pareto Curve will cover a good range of data with smooth transition. The Pareto fronts obtained for target strength could be used as calibration charts for scheduling the soft soil stabilization activities.
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