A hybrid cellular automaton–bi-directional evolutionary optimization algorithm for topological optimization of crashworthiness

ABSTRACT

This article proposes a new algorithm for topological optimization under dynamic loading which combines cellular automata with bi-directional evolutionary structural optimization (BESO). The local rules of cellular automata are used to update the design variables, which avoids the difficulty of obtaining gradient information under nonlinear collision conditions. The intermediate-density design problem of hybrid cellular automata is solved using the BESO concept of 0–1 binary discrete variables. Some improvement strategies are also proposed for the hybrid algorithm to solve certain problems in nonlinear topological optimization, e.g. numerical oscillation. Some typical examples of crashworthiness problems are provided to illustrate the efficiency of the proposed method and its ability to find the final optimal solution. Finally, numerical results obtained using the proposed algorithms are compared with reference examples taken from the literature. The results show that the hybrid method is computationally efficient and stable.

KEYWORDS:

• Cellular automaton
• bi-directional evolutionary structural optimization
• crashworthiness
• topological optimization

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Key R&D Program of China [number 2016YFB1200602-33], the National Natural Science Foundation of China [U1334208, 51275532] and the National Science and Technology Support Program [number 2015BAG13B01-05].