Optimization Design Of Bridge Crane Girder Based On Improved Artificial Bee Colony Algorithm

As important overloads transport machinery, bridge crane plays an important role in all areas of the manufacturing; its lightweight design is the industry trend. Therefore, using intelligent algorithm to optimize the crane structure, make the structure more compact, is of great significance. This paper proposes an optimization method based on an improved Artificial Bee Colony Algorithm(ABCA), and the method was carried out on the crane girder structure optimization.ABCA is a new type of swarm intelligence global optimization algorithm, possesses the characteristics of less control parameters, easy to implement and has been widely concerned. But with further research, the algorithms in the actual application process inevitably exposed some flaws. In this paper, on the basis of the basic ABCA for its slow convergence, large Searching blindness and other issues, the introduction of a adaptive step length, path exchanging neighborhood search and global optimization information of mutation strategy of differential evolution algorithm makes the convergence of the improved algorithms to strengthen and improve the convergence speed, improved the randomness, improves the optimization accuracy. For a planarization of late algorithm searching, the introduction of crossover and mutation of genetic algorithm, increase the diversity of population, to enhance the global stability of the algorithm. Using the girder of bridge crane as an example and establishing, the paper established the main girder optimization mathematical model, and then the improved ABCA was carried out on the main girder cross section size optimization, and then compare the optimization results of the basic ABCA. Final results show that by compared the girder which is optimized and the primitive girder the weight reduction is obvious. Finally, the optimized girder is simulated by Ansys software to verify the feasibility of the optimize result.