Improved multi-objective Hybrid Genetic Algorithm for Shape and Size Optimization of Free-form latticed structures

Abstract

Industrial and public buildings generally adopt spatial latticed structures as roofs, where the number of members is too large for the traditional genetic algorithm (GA) to conduct size optimization. In this paper, an improved multi-objective hybrid genetic algorithm incorporating the fully stressed design (FSD), called NSGA-II-FSD, is proposed for the shape and size optimization of large-scale free-form spatial latticed structures. The proposed method uses the B-spline theory, which makes it easier to generate free-form surfaces that meet the aesthetic requirements. The total mass and strain energy of the structure are selected as the objective functions, and the strength, as well as the stability of the member, are taken as the constraints. By incorporating the FSD, one can also set the stress ratio limit and get an optimal set of cross-sections of the members under the desired margin of safety. Besides, a significant mutation strategy is proposed in order to expand the searching boundary, which can avoid premature convergence. Through numerical examples of a cylindrical latticed shell and a free-form space truss , it is proved that the proposed method is effective, and engineers can choose the solution according to their preferences in practical design. • Method for shape and size optimization of latticed structures is proposed. • Proposed method can handle multi-objective optimization problems. • A significant mutation strategy is proposed to expand the searching boundary. • The improved FSD is effective to solve the problems with multiple load cases. • All individuals in Pareto front can be directly adopted by designers.