Optimization algorithms appear in the core calculations of numerous Artificial Intelligence (AI) and Machine Learning methods and Engineering and Business applications. Following recent works on AI’s theoretical deficiencies, a rigour context for the optimization problem of a black-box objective function is developed. The algorithm stems directly from the theory of probability, instead of presumed inspiration. Thus the convergence properties of the proposed methodology are inherently stable. In particular, the proposed optimizer utilizes an algorithmic implementation of the n-dimensional inverse transform sampling as a search strategy. No control parameters are required to be tuned, and the trade-off among exploration and exploitation is, by definition, satisfied. A theoretical proof is provided, concluding that when falling into the proposed framework, either directly or incidentally, any optimization algorithm converges. The numerical experiments verify the theoretical results on the efficacy of the algorithm apropos reaching the sought optimum.

优化算法出现在众多人工智能（AI）和机器学习方法以及工程和商业应用程序的核心计算中。继有关AI的理论缺陷的最新工作之后，为黑盒目标函数的优化问题开发了严格的环境。该算法直接基于概率理论，而不是假定的启发。因此，所提出的方法的收敛特性本质上是稳定的。特别地，所提出的优化器利用了n的算法实现。维逆变换采样作为搜索策略。不需要调整控制参数，并且根据定义，可以满足勘探与开发之间的权衡。提供了理论证明，得出的结论是，当直接或偶然落入建议的框架时，任何优化算法都将收敛。数值实验验证了算法达到预期最优效果的理论结果。