多策略协同改进的阿基米德优化算法及其应用

针对阿基米德优化算法（AOA）寻优过程中存在全局搜索能力弱、收敛精度低、易陷入局部最优等缺陷，提出一种融合多策略的阿基米德优化算法（MAOA）。首先，采用随机高斯变异策略选取适应度优的多个个体引导种群向最优解区域寻优，增强全局搜索能力；其次，利用多种混沌映射的随机性、遍历性和多样性，引入局部混沌搜索策略扩大混沌空间的搜索范围，提高算法的局部开发能力；同时，为了协调算法的全局勘探和局部开采能力，提出一种非线性动态密度降低因子；最后，利用Levy飞行引导机制的黄金正弦策略对种群位置进行扰动更新，增加迭代过程中种群的多样性，提高算法跳出局部最优的能力。通过对12个基准测试函数和部分CEC2014测试函数进行仿真实验，结果表明所提算法能够改善AOA全局探索能力弱、易陷入局部最优等缺点，提高AOA的寻优精度和稳定性。另外，引入机械设计案例进行测试分析，进一步验证MAOA在处理实际问题上的适用性和可行性。

Improved Archimedes optimization algorithm by multi-strategy collaborative and its application

n order to overcome the drawbacks of Archimedes optimization algorithm(AOA), such as weak global search ability, easily trapping into local optimum and prematurely converge, this paper put forward Archimedes optimization algorithm improved by multi-strategy collaborative(MAOA). Initially, it used the random Gaussian mutation strategy to increase the diversity of the population in the iterative process and strengthen the global search ability. Then, based on the randomness, ergodicity and diversity of multiple chaotic models, it introduced the local chaotic search strategy to expand the scope range of the chaotic space and enhance the local development capabilities of the algorithm. At the same time, it proposed a nonlinear dynamic density decreasing factor to coordinate the global exploration ability and local development ability of the algorithm. Finally, for the sake of increasing the diversity of the population during the iteration process, this paper adopted the golden sine strategy of the Levy flight guidance mechanism to perturb the population position and improve the ability of the algorithm to jump out of the local optimum. Through simulation experiments on 12 benchmark functions and part of the CEC2014 function set, the results show that the proposed algorithm can overcome the shortcomings of AOA''s weak global exploration ability and easy to fall into local optimality, and improve the accuracy and stability of AOA. In addition, the introduction of mechanical optimization design cases for testing and analysis will further verify the feasibility and applicability of MAOA on practical issues.