自适应t分布与动态边界策略改进的算术优化算法

针对算术优化算法（arithmetic optimization algorithm， AOA）存在的收敛速度慢、易陷入局部最优等问题，提出了自适应t分布变异和动态边界策略改进的算术优化算法（t-CAOA）。利用引入自适应t分布变异策略提高种群的多样性和质量可以有效提升算法的收敛速度，同时通过引入余弦控制因子的动态边界策略优化AOA的寻优过程，从而协调AOA的全局勘探和局部开发能力。对10个单模态和多模态函数进行寻优实验，并与鲸鱼优化算法（whale optimization algorithm）、灰狼优化算法（grey wolf optimizer）等算法进行对比，实验结果表明，经过改进的算术优化算法具有更高的寻优精度和稳定性。进一步对t-CAOA进行求解大规模优化问题的实验，实验结果表明改进过的t-CAOA可以有效地解决大规模优化问题。

Arithmetic optimization algorithm based on adaptive t-distribution and improved dynamic boundary strategy

In order to solve the problems of slow convergence and easy to fall into local optimization in arithmetic optimization algorithm(AOA), this paper proposed an arithmetic optimization algorithm with adaptive t-distribution mutation and dynamic boundary strategy improvement(t-CAOA). The introduction of adaptive t-distribution mutation strategy to improve the diversity and quality of the population could effectively improve the convergence speed of the algorithm. At the same time, it introduced the dynamic boundary strategy of cosine control factor to optimize the optimization process of AOA, so as to coordinate the global exploration and local development ability of AOA. It performed of optimization experiments on 10 optimal modal and multi-modal functions, and compared them with whale optimization algorithm, grey wolf optimizer and other algorithms. The experimental results show that the improved arithmetic optimization algorithm has higher optimization accuracy and stability. Further experiments on t-CAOA to solve large-scale optimization problems, the experimental results show that the improved t-CAOA can effectively solve large-scale optimization problems.