Improved backtracking search algorithm for pseudo dynamic active earth pressure on retaining wall supporting c-Ф backfill

Display Omitted New self-adaptive control parameters setting are incorporating to Backtracking Search Algorithm for improving the performance of the algorithm.To validate the performance of the proposed algorithm, it is applied to solve twenty five CEC2005 test functions.The proposed method has been successfully applied to obtain the active earth pressure on retaining wall supporting c- backfill using the pseudo dynamic method. Optimization algorithms are effective and powerful tools for solving the non-linear optimization problems. Backtracking Search Optimization Algorithm (BSA) is a newly proposed Evolutionary Algorithm (EA) and has been applied to optimize different complex optimization problems in science and engineering. In the present study, a new adaptive control parameter based Improved Backtracking Search Optimization Algorithm (IBSA) is suggested. Due to the validation of the suggested method, it has been applied to CEC2005 benchmark functions and the simulation results are compared with different existing algorithms. Also, it has been used to determine active earth pressure on retaining wall supporting c- backfill using the pseudo dynamic method. Simulation result shows that the proposed method is suitable to solve such type of problems and the results obtained are found satisfactory.

[1]  A. Goh Genetic algorithm search for critical slip surface in multiple-wedge stability analysis , 1999 .

[2]  P. N. Suganthan,et al.  Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[3]  Marjan Mernik,et al.  Replication and comparison of computational experiments in applied evolutionary computing: Common pitfalls and guidelines to avoid them , 2014, Appl. Soft Comput..

[4]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[5]  A. E. Eiben,et al.  Efficient relevance estimation and value calibration of evolutionary algorithm parameters , 2007, 2007 IEEE Congress on Evolutionary Computation.

[6]  Christian Blum,et al.  Ant colony optimization: Introduction and recent trends , 2005 .

[7]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[8]  R. Venkata Rao,et al.  Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems , 2011, Comput. Aided Des..

[9]  Paul McCombie,et al.  The use of the simple genetic algorithm in finding the critical factor of safety in slope stability analysis , 2002 .

[10]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

[11]  Haibin Duan,et al.  Adaptive Backtracking Search Algorithm for Induction Magnetometer Optimization , 2014, IEEE Transactions on Magnetics.

[12]  Liang Li,et al.  Particle swarm optimization algorithm for the location of the critical non-circular failure surface in two-dimensional slope stability analysis , 2007 .

[13]  Vivek K. Patel,et al.  Adaptive symbiotic organisms search (SOS) algorithm for structural design optimization , 2016, J. Comput. Des. Eng..

[14]  Thomas Stützle,et al.  A Racing Algorithm for Configuring Metaheuristics , 2002, GECCO.

[15]  Feng Liu,et al.  Group Search Optimization for Applications in Structural Design , 2011 .

[16]  Saime Akdemir Akar,et al.  Determination of optimal parameters for bilateral filter in brain MR image denoising , 2016, Appl. Soft Comput..

[17]  Behrouz Ahmadi-Nedushan,et al.  Optimal Design of Reinforced Concrete Retaining Walls using a Swarm Intelligence Technique , 2009 .

[18]  Kwok Yip Szeto,et al.  Adaptive Genetic Algorithm with Mutation and Crossover Matrices , 2007, IJCAI.

[19]  Andries Petrus Engelbrecht,et al.  Self-adaptive Differential Evolution , 2005, CIS.

[20]  Marjan Mernik,et al.  Exploration and exploitation in evolutionary algorithms: A survey , 2013, CSUR.

[21]  Peter John Cleall,et al.  An efficient approach for locating the critical slip surface in slope stability analyses using a real-coded genetic algorithm , 2010 .

[22]  Sima Ghosh,et al.  Experimental Study and Pseudo-dynamic Solution for Seismic Active Earth Pressure on Model Retaining Wall supporting c-F Backfill , 2013 .

[23]  Mehmet Fatih Tasgetiren,et al.  Differential evolution algorithm with ensemble of parameters and mutation strategies , 2011, Appl. Soft Comput..

[24]  Harish Sharma,et al.  Self-adaptive artificial bee colony , 2014 .

[25]  Sima Ghosh,et al.  Parameters Optimization of Geotechnical Problem Using Different Optimization Algorithm , 2015, Geotechnical and Geological Engineering.

[26]  A. E. Eiben,et al.  A method for parameter calibration and relevance estimation in evolutionary algorithms , 2006, GECCO '06.

[27]  Anurag Mohanty SLOPE STABILITY ANALYSIS USING GENETIC ALGORITHM , 2009 .

[28]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[29]  Dervis Karaboga,et al.  On clarifying misconceptions when comparing variants of the Artificial Bee Colony Algorithm by offering a new implementation , 2015, Inf. Sci..

[30]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[31]  Janez Brest,et al.  Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems , 2006, IEEE Transactions on Evolutionary Computation.

[32]  Andrew C. Heath,et al.  Simple genetic algorithm search for critical non-circular failure surface in slope stability analysis , 2005 .

[33]  Prasid Syam,et al.  An ant colony system based control of shunt capacitor banks for bulk electricity consumers , 2016, Appl. Soft Comput..

[34]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[35]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.

[36]  Qingfu Zhang,et al.  Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters , 2011, IEEE Transactions on Evolutionary Computation.

[37]  Janez Brest,et al.  Genetic algorithm with advanced mechanisms applied to the protein structure prediction in a hydrophobic-polar model and cubic lattice , 2016, Appl. Soft Comput..

[38]  Marjan Mernik,et al.  A chess rating system for evolutionary algorithms: A new method for the comparison and ranking of evolutionary algorithms , 2014, Inf. Sci..

[39]  Mehdi Mousavi,et al.  Slope stability analyzing using recent swarm intelligence techniques , 2015 .

[40]  Sima Ghosh,et al.  A new ensemble algorithm of differential evolution and backtracking search optimization algorithm with adaptive control parameter for function optimization , 2016 .

[41]  Xin Yao,et al.  Self-adaptive differential evolution with neighborhood search , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[42]  Aniruddha Sengupta,et al.  Locating the critical failure surface in a slope stability analysis by genetic algorithm , 2009, Appl. Soft Comput..

[43]  Mauro Birattari,et al.  Tuning Metaheuristics - A Machine Learning Perspective , 2009, Studies in Computational Intelligence.

[44]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[45]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[46]  Kalyanmoy Deb,et al.  Optimizing Engineering Designs Using a Combined Genetic Search , 1997, ICGA.

[47]  Zbigniew Michalewicz,et al.  Parameter Control in Evolutionary Algorithms , 2007, Parameter Setting in Evolutionary Algorithms.

[48]  K. M. Neaupane,et al.  Determination of the critical failure surface for slope stability analysis using ant colony optimization , 2009 .

[49]  Pinar Çivicioglu,et al.  Backtracking Search Optimization Algorithm for numerical optimization problems , 2013, Appl. Math. Comput..

[50]  Xin Yao,et al.  Scalability of generalized adaptive differential evolution for large-scale continuous optimization , 2010, Soft Comput..

[51]  Matej Crepinsek,et al.  A note on teaching-learning-based optimization algorithm , 2012, Inf. Sci..

[52]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[53]  Soorathep Kheawhom,et al.  Efficient constraint handling scheme for differential evolutionary algorithm in solving chemical engineering optimization problem , 2010 .

[54]  Marjan Mernik,et al.  Is a comparison of results meaningful from the inexact replications of computational experiments? , 2016, Soft Comput..

[55]  Xin Yao,et al.  A new self-adaptation scheme for differential evolution , 2014, Neurocomputing.

[56]  Mahmoud Ghazavi,et al.  Optimization of Reinforced Concrete Retaining Walls Using Ant Colony Method , 2011 .

[57]  H. Abbass The self-adaptive Pareto differential evolution algorithm , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[58]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.