Abstract: Abstract： To address the problems of uneven initial population distribution， poor individual adaptability and the tendency to fall into local optimality in bald eagle search algorithm， an improved bald eagle search algorithm is proposed for solving function optimization problems. Firstly， the Circle chaos mapping strategy is introduced in the initialization phase to enrich the diversity of the initial bald eagle individuals. The nonlinear weights are introduced to break the inherent linear search pattern of bald eagle individuals in the selected search space phase， and adaptively adjust the ability of the algorithm to search and exploit. Secondly， the bald eagle leader learns dynamically from the representative bald eagle individuals in the best search position. The purpose is to update the individual adaptive bald eagles during the spiral search. Finally， the Gaussian variation strategy is executed for the bald eagle individuals in the best search position， and the bald eagle leader individuals in the curve swoop process are updated iteratively according to the size of individual fitness， and the fitness of most bald eagle individuals in the population is enhanced， which can avoid the stagnation situation of algorithm in the function search to a certain extent. Based on some benchmark test functions and comparative experiments of some CEC2017 functions， the superiority of the algorithm proposed in this paper is verified.

Key words: Key words： bald eagle search algorithm, Circle chaotic map, non-linear weight, dynamic learning, Gaussian mutation