Improved metaheuristics through migration-based search and an acceptance probability for truss optimization - Kumar, Sumit; Tejani, Ghanshyam G.; Pholdee, Nantiwat; Bureerat, Sujin (Asian journal of civil engineering. Building and housing, 2020)
The proposed study investigated the simultaneous size and topology optimization of planar and spatial truss design subject to static and dynamic constraints. Considering practical construction, discrete cross-sectional areas of the standard design problems are taken. Also, the truss design problems are deemed to have several loading conditions under bounds such as component stresses, natural frequencies, nodal displacements, Euler buckling parameters, and kinematic stability requirements. Topology optimization of trusses leads to the elimination of superfluous components and nodes from the ground structure (known as highly hyperstatic truss), which in turn reduces the overall weight of the truss. Owing to the superfluous number of analyses and singular resolution, the complexities emerge in this technique; thus, the Grubler’s criterion for mobility check and positive definiteness for stability check is applied. Five improved metaheuristics (viz. the improved dragonfly algorithm, improved whale optimization algorithm, improved ant lion optimizer, improved heat transfer search, and improved teaching–learning-based optimization) which are based on a random-migration search and simulated annealing (SA)-based selection have been implemented for solving such challenging issues. The proposed algorithms are applied to three benchmark problems (i.e. 20-bar, 24-bar, and 72-bar (3D) truss problems), and the obtained results are compared with basic optimizers which manifest the superiority in the performance of the proposed techniques. The statistical analysis of the experimental work has been carried out by conducting Friedman’s rank test. Eventually, the results justify the harmony between the local intensification and global diversification of the modified optimizers.