Multi-objective optimization of foundation using global-local gravitational search algorithm

【This paper introduces a novel optimization technique based on gravitational search algorithm (GSA) for numerical optimization and multi-objective optimization of foundation. In the proposed method, a chaotic time varying system is applied into the position updating equation to increase the global exploration ability and accurate local exploitation of the original algorithm. The new algorithm called global-local GSA (GLGSA) is applied for optimization of some well-known mathematical benchmark functions as well as two design examples of spread foundation. In the foundation optimization, two objective functions include total cost and $CO_2$ emissions of the foundation subjected to geotechnical and structural requirements are considered. From environmental point of view, minimization of embedded $CO_2$ emissions that quantifies the total amount of carbon dioxide emissions resulting from the use of materials seems necessary to include in the design criteria. The experimental results demonstrate that, the proposed GLGSA remarkably improves the accuracy, stability and efficiency of the original algorithm.】

[1]  Hossein Nezamabadi-pour,et al.  Disruption: A new operator in gravitational search algorithm , 2011, Sci. Iran..

[2]  Mustafa Sonmez,et al.  Discrete optimum design of truss structures using artificial bee colony algorithm , 2011 .

[3]  Saeed Gholizadeh,et al.  Optimum design of structures by an improved genetic algorithm using neural networks , 2005, Adv. Eng. Softw..

[4]  Mahdiyeh Eslami,et al.  Efficient gravitational search algorithm for optimum design of retaining walls , 2013 .

[5]  K. Lee,et al.  A new structural optimization method based on the harmony search algorithm , 2004 .

[6]  Siti Zaiton Mohd Hashim,et al.  Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm , 2012, Appl. Math. Comput..

[7]  Víctor Yepes,et al.  CO2-Optimization Design of Reinforced Concrete Retaining Walls Based on a VNS-Threshold Acceptance Strategy , 2012, J. Comput. Civ. Eng..

[8]  Ahmed El-Shafie,et al.  A modified gravitational search algorithm for slope stability analysis , 2012, Eng. Appl. Artif. Intell..

[9]  Luigi Fortuna,et al.  Chaotic sequences to improve the performance of evolutionary algorithms , 2003, IEEE Trans. Evol. Comput..

[10]  Barron J. Bichon,et al.  Design of Steel Frames Using Ant Colony Optimization , 2005 .

[11]  S. O. Degertekin Harmony search algorithm for optimum design of steel frame structures: A comparative study with other optimization methods , 2008 .

[12]  Fred H. Kulhawy,et al.  Economic Design Optimization of Foundations , 2008 .

[13]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[14]  Ahmed El-Shafie,et al.  Modified particle swarm optimization for optimum design of spread footing and retaining wall , 2011 .

[15]  R. W. Baines,et al.  An application of simulated annealing to the optimum design of reinforced concrete retaining structures , 2001 .

[16]  Charles V. Camp,et al.  Design of Retaining Walls Using Big Bang–Big Crunch Optimization , 2012 .

[17]  Xiangtao Li,et al.  A novel hybrid K-harmonic means and gravitational search algorithm approach for clustering , 2011, Expert Syst. Appl..

[18]  Robert M. May,et al.  Simple mathematical models with very complicated dynamics , 1976, Nature.