关键词: 群智能优化算法, 蝙蝠算法（BA）, 自适应策略, 动态邻域

Abstract:

Bat Algorithm（BA） is a new type of meta-heuristic algorithm. Aiming at the problems of reduced optimization accuracy and easy to trap into local optimum, an improved algorithm with adaptive Doppler strategy and dynamic neighborhood strategy SDDNBA is proposed. According to the relative distance between the bats and the prey during the predation process, the adaptive Doppler strategy is introduced to improve the parameter of frequency and enhance the optimization ability of the algorithm for global exploration. Meanwhile, the dynamic neighborhood strategy is combined with the BA. This strategy can increase the diversity of the algorithm’s optimization structure and avoid the algorithm falling into the local optimum. The convergence and computational complexity of the SDDNBA algorithm are theoretically analyzed. In the numerical experiment part, comparative experiments are carried out on the improved SDDNBA algorithm, and numerical simulation comparative experiments are carried out on 10 classic test functions in different dimensions. In addition, it is applied to the optimization design problem of coil compression spring and compared with other algorithms. The results fully prove the effectiveness of the improved algorithm, with better convergence speed, convergence accuracy and stability robustness.

Key words: swarm intelligence optimization algorithm, Bat Algorithm（BA）, adaptive strategy, dynamic neighborhood