Optimization Design Of Bridge Crane Girder Based On Improved Artificial Fish Swarm Algorithm

Bridge crane is widely applied as an indispensable lifting equipment in the company. Thetraditional design method of crane is too conservative resulting in the volume and weight ofthe metal structure becoming more bigger, which make resources waste.For this reasonfinding a new design method to solve the above problems effectively that change crane intosmall volume, light weight and compact structure is a great significance for the developmentof the domestic crane industry. It is first to optimize the girder that as an important componentof the metal structures.Artificial Fish Swarm Algorithm (AFSA) is concerned by more and more scholars as aglobal optimization algorithm. But with further research, the algorithms in the actualapplication process inevitably exposed some flaws. By analyzing the AFSA propose animproved AFSA to solve shortcomings which search blindness and reaching the part bestvalue easily slow convergence caused by the search area flat in the later. Putting the crossoverand mutation of genetic algorithm into it and improving the AFSA by the global optimumoperator and self-adaptive concept and feedback behaviors to increase the diversity of thepopulation and the accuracy of optimization, enhance the ability of the algorithm out of localminima, while improving the flatness of the search area in the later phase to improve theconvergence rate. Using the girder of bridge crane as an example and establishing theoptimization model of girder optimize the sectional dimension by the improved AFSA anddetermine a set of reasonable parameters by comparing different parameters to compare theresults of basic AFSA. Finally, the optimized girder is simulated by Ansys software, theresults show that by compared the girder which is optimized and the primitive girder theweight reduction is obvious and verify the feasibility of the optimize result.