Cuckoo search algorithm for applied structural and design optimization: Float system for experimental setups

Abstract In the present study, Cuckoo Search (CS) as a nature-inspired optimization algorithm was applied for structural and design optimization of a new float system for experimental setups. For this purpose, based on the setup configuration, it was tried to minimize the total length of the float, while maintaining the structural and performance-based constraints. Different geometries for the float structure were examined to come up with the feasible options. The problem was formulated into a constrained optimization in terms of four or five variables, depending on the geometry, along with two performance-based constraints and some structural constraints. CS was used to solve the constrained optimization problem and the convergence trends of the parameters to optimal solutions were examined in details. Generalized reduced gradient (GRG) method known as GRG nonlinear was also used for validation and comparison purpose. The results of the optimization and the performance of the float produced showed that CS can be used as a powerful tool for applied structural and design problems. It should be mentioned that the float problem can be used as a benchmark structural design problem for validation of new optimization algorithms. Besides, the optimal float can be produced for various experimental setups with different structures and constraints, depending on the application.

[1]  Zhengqi Lu,et al.  Structural damage identification based on cuckoo search algorithm , 2015 .

[2]  Luca Maria Gambardella,et al.  A survey on metaheuristics for stochastic combinatorial optimization , 2009, Natural Computing.

[3]  Chiang Kao,et al.  Performance of several nonlinear programming software packages on microcomputers , 1998 .

[4]  Carmine Pappalettere,et al.  Metaheuristic Design Optimization of Skeletal Structures: A Review , 2010 .

[5]  M. H. Yas,et al.  Free vibration analysis of functionally graded annular plates by state-space based differential quadrature method and comparative modeling by ANN , 2012 .

[6]  M. H. Yas,et al.  Three-dimensional free vibration analysis of functionally graded piezoelectric annular plates via SSDQM and comparative modeling by ANN , 2013, Math. Comput. Model..

[7]  Mostafa Jalal,et al.  Soft computing techniques for compressive strength prediction of concrete cylinders strengthened by CFRP composites , 2013 .

[8]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[9]  Christian Blum,et al.  Metaheuristics in combinatorial optimization: Overview and conceptual comparison , 2003, CSUR.

[10]  Mostafa Jalal,et al.  Big data in nanocomposites: ONN approach and mesh-free method for functionally graded carbon nanotube-reinforced composites , 2018, J. Comput. Des. Eng..

[11]  Praveen Ranjan Srivastava,et al.  An Efficient Optimization Algorithm for Structural Software Testing , 2012 .

[12]  H. J. Xu,et al.  Structural damage identification based on modified Cuckoo Search algorithm , 2016 .

[13]  Xin-She Yang,et al.  Cuckoo search: recent advances and applications , 2013, Neural Computing and Applications.

[14]  S. Janson,et al.  Wiley‐Interscience Series in Discrete Mathematics and Optimization , 2011 .

[15]  Mostafa Jalal,et al.  RETRACTED ARTICLE: Application of genetic programming (GP) and ANFIS for strength enhancement modeling of CFRP-retrofitted concrete cylinders , 2012, Neural Computing and Applications.

[16]  Mehmet Polat Saka,et al.  Optimum Design of Steel Frames using Stochastic Search Techniques Based on Natural Phenomena: A Review , 2007 .

[17]  Mostafa Jalal,et al.  Prediction of load-displacement curve of concrete reinforced by composite fibers (steel and polymeric) using artificial neural network , 2010, Expert Syst. Appl..

[18]  Xin-She Yang,et al.  Nature-Inspired Metaheuristic Algorithms , 2008 .

[19]  M. Noel,et al.  A new gradient based particle swarm optimization algorithm for accurate computation of global minimum , 2012, Appl. Soft Comput..

[20]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[21]  Xin-She Yang,et al.  Optimization and Metaheuristic Algorithms in Engineering , 2013 .

[22]  Mostafa Jalal,et al.  Multiobjective optimization of composite cylindrical shells for strength and frequency using genetic algorithm and neural networks , 2014 .

[23]  Shiyuan Yang,et al.  Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm , 2007, Inf. Process. Lett..

[24]  Yang Song-ming,et al.  Markov Model and Convergence Analysis Based on Cuckoo Search Algorithm , 2012 .

[25]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[26]  Mojtaba Fathi,et al.  Compressive strength prediction by ANN formulation approach for CFRP confined concrete cylinders , 2015 .

[27]  Ali Akbar Ramezanianpour,et al.  Strength enhancement modeling of concrete cylinders confined with CFRP composites using artificial neural networks , 2012 .

[28]  M. P. Saka,et al.  Recent Developments in Metaheuristic Algorithms: A Review , 2012 .

[29]  A. K. Mukhopadhyay,et al.  Bat algorithm as a metaheuristic optimization approach in materials and design: optimal design of a new float for different materials , 2018, Neural Computing and Applications.

[30]  Arne Drud,et al.  CONOPT: A GRG code for large sparse dynamic nonlinear optimization problems , 1985, Math. Program..

[31]  B. H. V. Topping.  Civil Engineering Computations: Tools and Techniques , 2008 .

[32]  Xin-She Yang,et al.  Metaheuristics in water, geotechnical and transport engineering , 2012 .

[33]  Anal K. Mukhopadhyay,et al.  Design, manufacturing, and structural optimization of a composite float using particle swarm optimization and genetic algorithm , 2019 .

[34]  Maral Goharzay,et al.  A worldwide SPT-based soil liquefaction triggering analysis utilizing gene expression programming and Bayesian probabilistic method , 2017 .

[35]  Kenneth Morgan,et al.  Modified cuckoo search: A new gradient free optimisation algorithm , 2011 .