Research On Evolutionary Algorithm For Structural Dynamic Design Optimization

Dynamic optimal design of structural is in the field of the current cutting-edge issues. Reasonable structural optimization not only can reduce the weight of structures and the costs of materials, but also the strength, stiffness, vibration behavior and other performances of structures can be improved. Structural optimization is an important research direction in modern design field.Evolutionary algorithm is a global optimization algorithm. Because evolutionary algorithms do not involve any gradient information and sensitivity analysis, and do not require tedious gradient formula for a variety of different types of structural optimization and it only involves the design variables in computing operation code. And it can handle various different types of variables flexibility. These features make the evolutionary algorithm is very suitable for the development of a versatile global optimization algorithm, for solving structural dynamic optimization problems.Guo Tao algorithm is an evolutionary algorithm which was proposed by Guo Tao and Kang Li-shan in 1999 for solving constrained nonlinear programming problems, and the core is a multi-parent crossover operator. Many scholars practice shows that, Guo Tao algorithm has some advantages, such as simple computation, huge search space, high precision, and it can search multiple optimal solutions, etc; At the same time, because the algorithm only has multi-parent crossover operator, which shows that the global search capability is more performance and the local search capacity is weak, and thus slow convergence project hinders its further application. Based on the above characteristics of Guo Tao algorithm, this paper hopes to improve the algorithm of Guo Tao. We will propose a new hybrid evolutionary algorithm which can absorb the benefits of Guo Tao algorithm and improve the shortcomings. The main contents of this article are as follows:Firstly, based on the study of basic genetic algorithm and the recent study on the progress of Guo Tao algorithm, this paper will propose a number of improvements to propose a new hybrid evolutionary algorithm. Secondly, the proposed hybrid evolutionary algorithm will be applied to a number of structural dynamic optimization problems, and by comparing the experimental results show that the proposed improvements are feasible and effective.