Multi-mode Evolution Genetic Algorithm and Optimizaton of Thin-Walled Tube with Folding Patterns Baesd on Crashworthiness
SUN Hailong1, SUI Yunkang1,2, YE Hongling1
1. College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100024, China;2. State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology , Dalian 116023, Liaoning Province, China
Abstract：A multi-mode evolution genetic algorithm is proposed to improve the simple genetic algorithm and to solve the crashworthiness optimization design problems for a new type of thin-walled tube with folding patterns. Structural crashworthiness analysis is a highly non-linear transient dynamic process, involving a very complex relationship between the objective function (energy absorption value) and the design variables (wave height and angle) without an explicit function expression to optimize the design of the thin-walled tube. The optimization model of the thin-walled tube established by the Response Surface Method (RSM) and various encoder modes, the selection strategy, the crossover and the mutation operation are used in the multi-mode evolution improved genetic algorithm, and an inversion operation, similar to the genetic variation in the chromosome structure, effectively improves the population diversity. Examples of function test results show that the algorithm overcomes the local convergence of the GA and increases the convergent speed. The application of the multi-mode evolution genetic algorithm for crashworthiness optimization of the thin-walled tube with folding patterns shows the efficiency and convenience of this algorithm to solve such optimization problems. The energy absorption of the optimized structure is increased by more than 40% , a significant improvement over the initial design.
2012, Vol. 30 
