非均匀变异的互利自适应缎蓝园丁鸟优化算法

针对缎蓝园丁鸟优化（SBO）算法求解精度不高和收敛速度慢等问题，提出一种改进的缎蓝园丁鸟优化（ISBO）算法。首先，引入非均匀变异算子，动态地调整每次迭代园丁鸟个体的搜索步长，使算法能快速高效地寻求全局最优值；其次，采用互利因子对算法的社会部分引入更多组合模式，使其不再单一围绕前一个园丁鸟附近搜索，以获取更好的最优解；最后，为了更好地平衡算法的局部与全局搜索能力，引入余弦变化的惯性权重因子来更新园丁鸟的位置公式。使用收敛速度分析、Wilcoxon检验和8个基准函数对5种算法搜索性能进行对比分析，来评估改进缎蓝园丁鸟优化算法的效率。结果表明，改进算法具有更好的全局搜索能力和求解鲁棒性，同时寻优精度和收敛速度也比原来算法有所增强。

A mutually beneficial adaptive satin bowerbird optimization algorithm based on non-uniform mutation

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.