Self-Adaptive Doppler Compensation and Mutation Choice of Bat Algorithm

Abstract

This paper presents an improved bat algorithm (Doppler and mutant bat algorithm, DMBA) to overcome the problems of high dimensional optimization with low precision and premature convergence. A self-adaptive compensation for the Doppler effect of frequency is introduced according to the relative distance between the bat and the prey, and the flight direction is modified by combining the velocity offset mechanism to generate a new position close to the prey. Then, self-adaptive mutation choice strategies are constructed for the best individual. The larger step size generated by Cauchy mutation is used to get rid of the constraint of local extreme value, and then the smaller step size generated by Gaussian mutation is used to search the optimal region. Finally, the global exploration and local exploitation ability are balanced by adjusting loudness and pulse emission. The convergence and computational complexity of the algorithm are analyzed theoretically. 12 classical benchmark functions are simulated in different dimensions and the proposed algorithm is compared with other recent bat algorithms. The experimental results show that the proposed algorithm has better convergence speed and precision for solving high dimensional optimization problems.

Keywords

• bat algorithm
• doppler compensation
• velocity shift
• mutation choice