Multi-Objective Dynamic Optimum Of Gantry Container Crane Structure System

In view of the importance of multi-objective optimization in engineering, economy, management, military and so on, the research on multi-objective optimization has been paid more attention. It has developed into a new branch of science and demonstrated powerful vitality in application. The genetic algorithm is a global optimization, auto-adapted, probability-based searching algorithm which uses the experience of biological natural selection and genetic mechanism for reference. Owing to its unique superiority and robustness in solving the complex system optimization, it becomes a very effective method in solving multi-objective optimization problems.The dynamic behaviour of gantry container crane structure system is very difficult to be explicitly expressed with design variables. As a result of its powerful nonlinear mapping ability, multi-layer neural networks are used to describe and deal with the relations between design variables and dynamic parameters of the structure system. Once the neural networks model is built, it can substitute the finite element model, and be used for the reanalysis of structure dynamic behaviour. The analysis process is simple and direct. Moreover, computation speed of the neural networks model is faster than the finite element model, which applies to engineers and technicians, especially. Therefore, genetic algorithm is used to optimum the built neural networks model, to get the design variables and target values in the feasible zone when the dynamic behaviour is optimal.In this paper, BP neural networks are employed in combination with finite element analysis and orthogonal experiment method, to build the mathematical models of the vibration system for a rapid re-analysis. And genetic algorithm is used to optimum the neural networks model. Eventually, we get part of the Pareto optimal solutions.