Abstract: To solve the problem that the satin bowerbird optimizer (SBO) is prone to low accuracy and slow convergence, this paper proposes an improved satin bowerbird optimizer (ISBO). Firstly, the non-uniform mutation operator is introduced to dynamically adjust the search step size of each iteration bowerbird, so that the algorithm can quickly and efficiently find the global optimal value. Secondly, the mutually beneficial factor is used to introduce more combinatorial modes to the social part of the algorithm so as to no longer searches around the previous bowerbird, thus obtaining a better optimal solution. Finally, in order to better balance the local and global search ability of the algorithm, the inertia weight factor of cosine change is introduced to update the bowerbird position formula. Convergence rate analysis, Wilcoxon test and 8 benchmark functions are used to evaluate the efficiency of the improved satin bowerbird optimization algorithm. The results show that the improved algorithm has better global search capability and solution robustness, and the optimization precision and convergence speed are also better than the original algorithm. 

Key words: non-uniform mutation, mutually beneficial factor, inertia weight, function optimization, satin bowerbird optimization algorithm