关键词: 蝙蝠算法, 分数阶, 阿基米德螺旋, Lé, vy飞行, 减速器设计问题

Abstract:

In order to overcome the shortcomings which the search efficiency of the bat algorithm is lowing and it is easy to fall into local optimum in solving multimodal and complex nonlinear problems, an improved bat algorithm is proposed in this paper. The fractional order strategy with short-term memory characteristics is introduced to update bat position so as to increase population diversity and improve the convergence speed of the algorithm. A new solution is generated locally by the Archimedes spiral with Lévy flight strategy, which enhances the local exploitation ability and helps the algorithm jump out of the local optimum. The new nonlinear dynamic mechanism for adjusting loudness and pulse emission rate is to balance the exploration and exploitation abilities of the algorithm. The CEC2014 benchmark functions including unimodal, multimodal, hybrid and composition functions is selected to test the proposed algorithm and other swarm intelligence algorithms. The results show that the search efficiency and solution accuracy of the proposed algorithm are obviously improved compared with contrast algorithms. The superiority of the algorithm is verified by Friedman statistical analysis. Finally, the proposed algorithm is used to solve the design problem of mechanical engineering reducer. The experiment results verify the effectiveness of the proposed algorithm compared with PSO-DE, WCA, and APSO.

Key words: bat algorithm, fractional order, Archimedes spiral, Lévy flight, speed reducer design problem