Dynamic ensemble multi-strategy based bald eagle search optimization algorithm: A controller parameters tuning approach

Abstract

To address the problems of bald eagle search algorithm (BES), easy fall into local optimums, limited diversity and slow convergence, a dynamic ensemble multi-strategy bald eagle search (DMBES) algorithm is proposed. Firstly, a nonlinear control factor is constructed to explore the potential range of population search, which helps to explore potential solutions. Next, a Levy flight strategy is introduced to better smooth the transition and explore the solution space through individual information interaction. Then, a dynamic selection strategy is proposed to help individuals obtain optimal solutions and better balance the algorithm's exploration and exploitation capabilities. Finally, in numerical experiments, classical benchmark functions and CEC 2022 test functions are selected to compare the performance of DMBES with other reliable and recent algorithms. In addition, box plots, Wilcoxon signed-rank test and Friedman rank test are used to confirm the significance of the results. In the controller parameter tuning experiments, the results show that the optimized controller of the DMBES algorithm outperforms the six comparison algorithms in terms of overshoot and rise time.