论文标题
理论框架和灰狼优化器的一些有希望的发现，第二部分：全球融合分析
A theoretical framework and some promising findings of grey wolf optimizer, part II: global convergence analysis
论文作者
论文摘要
本文提出了基于几个有趣的理论发现的灰狼优化器（GWO）的理论框架，涉及采样分布，顺序1和订单-2稳定性以及全球收敛分析。在本文的第二部分中，全局收敛分析是根据众所周知的停滞假设进行的，以简化。首先，将停滞假设下的GWO的全局收敛性属性抽象并建模为两个命题，对应于全局搜索能力分析和概率-1全局收敛分析。然后，进行全球搜索能力分析。接下来，基于在停滞假设下的GWO新解的中心时刻的特征，证明了GWO在停滞假设下的概率1全局收敛性。最后，通过数值模拟验证了所有结论，并且在原始GWO中仍然可以保证无需停滞假设的全局收敛属性的相应讨论。
This paper proposes a theoretical framework of the grey wolf optimizer (GWO) based on several interesting theoretical findings, involving sampling distribution, order-1 and order-2 stability, and global convergence analysis. In the part II of the paper, the global convergence analysis is carried out based on the well-known stagnation assumption for simplification purposes. Firstly, the global convergence property of the GWO under stagnation assumption is abstracted and modelled into two propositions, corresponding to global searching ability analysis and probability-1 global convergence analysis. Then, the global searching ability analysis is carried out. Next, based on a characteristic of the central moments of the new solution of the GWO under stagnation assumption, the probability-1 global convergence property of the GWO under stagnation assumption is proved. Finally, all conclusions are verified by numerical simulations, and the corresponding discussions that the global convergence property can still be guaranteed in the original GWO without stagnation assumption are given.
