Steel frame optimal design using MHBMO algorithm

Various deterministic and stochastic algorithms have been used as optimization tools in different engineering problems over the last decade. In this regard, the Modified Honey Bee Mating Optimization (MHBMO) algorithm may be considered as a typical swarm-based approach for optimizing numerous problems in engineering fields. In this paper, a design procedure based on the MHBMO technique was developed for discrete optimization of frames consisting W-shapes. The objective function in this research is to obtain the minimum weight of frames subjected to both strength and displacement requirements imposed by the American Institute for Steel Construction (AISC) and Load Resistance Factor Design (LRFD). Several frame examples from the literature were examined to verify not only the suitability of the design procedure but also the robustness of the MHBMO algorithm for frame structure design. The optimum results obtained by the MHBMO algorithm performs the best in comparison with other available techniques in the literature for all three steel frames. In conclusion, the results shows that the MHBMO algorithm is a powerful and applicable optimization method for design of frames consisting W-shapes.

[1]  Taher Niknam,et al.  An efficient algorithm for multi-objective optimal operation management of distribution network considering fuel cell power plants , 2011 .

[2]  Vedat Toğan,et al.  Design of planar steel frames using Teaching–Learning Based Optimization , 2012 .

[3]  Majid Niazkar,et al.  Optimum Design of Lined Channel Sections , 2015, Water Resources Management.

[4]  Siamak Talatahari,et al.  An improved ant colony optimization for the design of planar steel frames , 2010 .

[5]  Ming-Huwi Horng,et al.  Image vector quantization algorithm via honey bee mating optimization , 2011, Expert Syst. Appl..

[6]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[7]  Seied Hosein Afzali,et al.  New Model for Determining Local Scour Depth Around Piers , 2016 .

[8]  Seied Hosein Afzali,et al.  Variable-Parameter Muskingum Model , 2016 .

[9]  Ayse T. Daloglu,et al.  An improved genetic algorithm with initial population strategy and self-adaptive member grouping , 2008 .

[10]  Helio J. C. Barbosa,et al.  A genetic algorithm encoding for cardinality constraints and automatic variable linking in structural optimization , 2008 .

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

[12]  A. Kaveh,et al.  Sizing, geometry and topology optimization of trusses via force method and genetic algorithm , 2008 .

[13]  R. E. Page,et al.  The evolution of multiple mating behavior by honey bee queens (Apis mellifera L.). , 1980, Genetics.

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

[15]  M. Niazkar,et al.  Assessment of Modified Honey Bee Mating Optimization for Parameter Estimation of Nonlinear Muskingum Models , 2015 .

[16]  Barry J. Adams,et al.  Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation , 2007, J. Frankl. Inst..

[17]  Georgios Dounias,et al.  Honey bees mating optimization algorithm for the Euclidean traveling salesman problem , 2011, Inf. Sci..

[18]  Hussein A. Abbass,et al.  A Monogenous MBO Approach to Satisfiability , 2001 .

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

[20]  H A Abbass,et al.  MARRIAGE IN HONEY-BEE OPTIMIZATION (MBO): A HAPLOMETROSIS POLYGYNOUS SWARMING APPROACH , 2001 .

[21]  Alex Elvin,et al.  An algorithm for grouping members in a structure , 2010 .

[22]  Feng Liu,et al.  A heuristic particle swarm optimization method for truss structures with discrete variables , 2009 .

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

[24]  S. O. Degertekin.  Optimum design of steel frames using harmony search algorithm , 2008 .