Optimum design of steel sway frames to BS5950 using harmony search algorithm

Abstract Harmony search method based optimum design algorithm is presented for the steel sway frames. The harmony search method is a numerical optimization technique developed recently that imitates the musical performance process which takes place when a musician searches for a better state of harmony. Jazz improvisation seeks to find musically pleasing harmony similar to the optimum design process which seeks to find the optimum solution. The optimum design algorithm developed imposes the behavioral and performance constraints in accordance with BS5950. The member grouping is allowed so that the same section can be adopted for each group. The combined strength constraints considered for a beam–column take into account the lateral torsional buckling of the member. The algorithm presented selects the appropriate sections for beams and columns of the steel frame from the list of 64 Universal Beam sections and 32 Universal Column sections of the British Code. This selection is carried out so that the design limitations are satisfied and the weight of steel frame is the minimum. The number of design examples considered to demonstrate the efficiency of the algorithm is presented.

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

[2]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[3]  G. Flake The Computational Beauty of Nature , 1998 .

[4]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[5]  Shahram Pezeshk,et al.  Optimized Design of Two-Dimensional Structures Using a Genetic Algorithm , 1998 .

[6]  Hojjat Adeli,et al.  Distributed Genetic Algorithm for Structural Optimization , 1995 .

[7]  Z. Geem Optimal Design of Water Distribution Networks Using Harmony Search , 2009 .

[8]  Saeed Shojaee,et al.  Optimal design of skeletal structures using ant colony optimization , 2007 .

[9]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[10]  D. Dasgupta Artificial Immune Systems and Their Applications , 1998, Springer Berlin Heidelberg.

[11]  Barry Hilary Valentine Topping,et al.  Parallel simulated annealing for structural optimization , 1999 .

[12]  A. Dhingra,et al.  Single and multiobjective structural optimization in discrete‐continuous variables using simulated annealing , 1995 .

[13]  Zong Woo Geem,et al.  Harmony Search Optimization: Application to Pipe Network Design , 2002 .

[14]  Zong Woo Geem,et al.  A New Heuristic Optimization Algorithm: Harmony Search , 2001, Simul..

[15]  O. Hasançebi,et al.  Optimal design of planar and space structures with genetic algorithms , 2000 .

[16]  Yi Min Xie,et al.  Evolutionary Structural Optimization , 1997 .

[17]  Aimo A. Törn,et al.  Global Optimization , 1999, Science.

[18]  Moshe Sipper An Introduction To Artificial Life , 1995 .

[19]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .

[20]  Mehmet Polat Saka,et al.  Genetic algorithm based optimum design of nonlinear planar steel frames with various semi- rigid connections , 2003 .

[21]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[22]  Mehmet Polat Saka,et al.  Optimum design of nonlinear steel frames with semi-rigid connections using a genetic algorithm , 2001 .

[23]  Guan-Chun Luh,et al.  Multi-objective optimal design of truss structure with immune algorithm , 2004 .

[24]  Shahram Pezeshk,et al.  Design of Nonlinear Framed Structures Using Genetic Optimization , 2000 .

[25]  Mehmet Polat Saka,et al.  Optimum spacing design of grillage systems using a genetic algorithm , 2000 .

[26]  P. Pardalos,et al.  Handbook of global optimization , 1995 .

[27]  Claus Mattheck,et al.  Design in Nature: Learning from Trees , 1998 .

[28]  Zong Woo Geem,et al.  Harmony search for structural design , 2005, GECCO '05.

[29]  R. Horst,et al.  Global Optimization: Deterministic Approaches , 1992 .

[30]  Barry Hilary Valentine Topping,et al.  Progress in Civil and Structural Engineering Computing , 2003 .

[31]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[32]  M. P. Saka Optimum Design of Skeletal Structures: A Review , 2003 .

[33]  V. K. Koumousis,et al.  Genetic Algorithms in Discrete Optimization of Steel Truss Roofs , 1994 .

[34]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[35]  Barry Hilary Valentine Topping,et al.  Advances in Engineering Computational Technology , 1998 .

[36]  Mehmet Polat Saka,et al.  Optimum Design of Grillage Systems Using Genetic Algorithms , 1998 .

[37]  Mehmet Polat Saka,et al.  Genetic algorithm based optimum bracing design of non-swaying tall plane frames , 2001 .

[38]  Leandro Nunes de Castro,et al.  Recent Developments In Biologically Inspired Computing , 2004 .

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

[40]  Charles V. Camp,et al.  Design of Space Trusses Using Ant Colony Optimization , 2004 .

[41]  Mehmet Polat Saka,et al.  Optimum design of pitched roof steel frames with haunched rafters by genetic algorithm , 2001 .

[42]  K. Lee,et al.  A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice , 2005 .

[43]  C. Adami,et al.  Introduction To Artificial Life , 1997, IEEE Trans. Evol. Comput..

[44]  Ray Paton Computing with biological metaphors , 1994 .

[45]  S. Rajeev,et al.  Discrete Optimization of Structures Using Genetic Algorithms , 1992 .