Design of Space Trusses Using Big Bang–Big Crunch Optimization

A Quantitative Comparison Between Size, Shape, Topology and Simultaneous Optimization for Truss Structures

“…This is not the heaviest structure possible from the selection of sections, but given the large range of section sizes and the results obtained a lighter structure which also satisfies the constraints was selected for the comparison. When comparing the result of 217 kg obtained for the size optimization with the 170 kg found by several other researchers (Jalili and Hosseinzadeh, 2015;Degertekin, 2013;Camp, 2007), there is a 28 % deficit. This may be due to a grouping discrepancy between the respective problem definitions.…”

Mixed Approaches to Handle Limitations and Execute Mutation in the Genetic Algorithm for Truss Size, Shape and Topology Optimization

“…This is identical to the best design developed in [10,51]. It performs better than others when the number of average weight for 50 runs are compared.…”

“…6) in different variants was performed in Refs. [7,10,16,44,[49][50][51] and in many other articles. This paper gives consideration to the condition of a 25-bar truss size optimization problem according to Ref.…”

“…Simulated annealing algorithm [2], bat-inspired algorithm [3], magnetic charged system search method [4], ant colony optimization [5], particle swarm optimizer [6], harmony search algorithm [7,8], charged system search method [9], and big bang-big crunch algorithm [10] are also in the class of such procedures. GAs have already found application in many areas of engineering and science [1,[11][12][13].…”

A Novel Implementation of Nature-inspired Optimization for Civil Engineering: A Comparative Study of Symbiotic Organisms Search

“…It is apparent that the design solution obtained by HS [31] is theoretically infeasible because these solutions violate the design constraint stated in [35]. The results produced by the SOS algorithm were competitive with those produced by TLBO [35] and SAHS [34] and superior to those of HPSO [32], and BB-BC [33]. Furthermore, the SOS algorithm delivered a better average solution, and lower standard deviation compared to the TLBO algorithm, supporting that the SOS algorithm is a better optimization method than TLBO in terms of consistency.…”