Shape optimization of free-form grid structures based on the sensitivity hybrid multi-objective evolutionary algorithm

In this paper, a novel shape optimization method is proposed to improve the mechanical performance of the free-form grid structures while satisfying the architectural requirements. The height of control points are considered as the optimization variables, the structural strain energy and the comprehensive quantitative index are considered as optimization objectives. The sensitivity hybrid multi-objective evolutionary algorithm (referred to as SH-MOEA) is developed and the shape optimization of the free-form grid structures is carried out based on the developed algorithm. The optimization results are compared with those based on NGSA-II, SPEA2 and MOEA/D algorithms. The results demonstrate that the developed algorithm not only can obtain the Pareto optimal solution set with better accuracy and uniformity, but also indicates higher computational efficiency than other three algorithms. The similarity of surface, the fluence and regularity of free-form grids are effectively improved by considering the geometric comprehensive quantitative index as the optimization objective.

Automated summary

A novel shape optimization method is proposed in this paper to improve the mechanical performance of free-form grid structures while satisfying architectural requirements. By considering control point height as optimization variables and structural strain energy and a comprehensive quantitative index as optimization objectives, a sensitivity hybrid multi-objective evolutionary algorithm (SH-MOEA) is developed for shape optimization. The results demonstrate that the developed algorithm outperforms other algorithms in terms of accuracy, uniformity, and computational efficiency, effectively improving the similarity of surface, fluence, and regularity of free-form grids.