融合Sin混沌和分段权值的阿基米德优化算法

针对阿基米德优化算法（Archimedes optimization algorithm，AOA）存在全局搜索能力弱、收敛精度低，易陷入局部最优等问题，提出融合Sin混沌和分段权值的阿基米德优化算法（SAOA）。采用无限折叠迭代的Sin混沌反向学习策略初始化种群，提高初始阶段解的质量，为全局搜索多样性奠定基础；引入算数交叉算子，将当前个体向与全局最优个体进行交叉，引导种群向最优解区域寻优，提高全局搜索能力；引入分段权值策略，平衡算法的全局勘探与局部开发能力，降低算法陷入局部最优的概率；通过对8个测试函数和部分CEC2014函数进行仿真实验及Wilcoxon秩和检验来评估改进算法的寻优性能，实验结果表明改进算法在搜索精度、收敛速度和稳定性等方面均有较大提升。另外，引入优化机械设计案例进行测试分析，进一步验证SAOA在工程优化问题上的可行性和适用性。

Archimedes Optimization Algorithm Combining Sin Chaos and Segmented Weights

Archimedes optimization algorithm combining Sin chaos and segmented weights（SAOA） is proposed to overcome the drawbacks of weak global search ability, low optimization precision and easily trapping into local optimum. In the proposed algorithm, the Sin chaotic opposition-based learning strategy is used to enhance the quality of the solution in the initial stage, which strengthens the diversity of population in the global searching process. Then, it introduces arithmetic crossover operator to cross the current individual direction with the global optimal individual, and guides the population to seek the optimal solution region. At the same time, segmented weight strategy is added to balance the global exploration and local development ability of the algorithm and reduce the probability of the algorithm falling into local optimum. Finally, the simulation experiments are conducted on the 8 benchmark functions and some part of CEC2014 functions and Wilcoxon rank sum test is conducted to evaluate the optimization performance of the improved algorithm. Simulation results show that the improved algorithm has greatly improved search accuracy, convergence speed and stability. In addition, the introduction of optimized mechanical design cases for testing and analysis will further verify the feasibility and applicability of SAOA in engineering optimization issues.