面向不可预测商业环境的自适应贝叶斯优化算法
Adaptive Bayesian Optimization Algorithm for Unpredictable Business Environments
AI总结：
针对不可预测商业环境中的动态优化问题，提出带自适应条件作用、自适应采集抖动和惩罚项鲁棒目标的贝叶斯优化框架，并在多类函数上验证其优于原始贝叶斯优化。
摘要：
本文提出了一种基于贝叶斯定理的创新优化框架和算法，其特点是自适应条件作用和抖动。自适应条件作用函数在每次迭代中动态修改均值目标函数，从而增强其适应性。均值函数表示模型对真实目标函数最优值的最佳估计，并根据观测数据进行调整。该框架还纳入了自适应采集抖动函数，通过调整采集函数的抖动来增强适应性。文中还引入了带有惩罚项的鲁棒目标函数，旨在在不确定性下生成鲁棒解。对该框架的评估包括单目标、解耦多目标和组合多目标函数。包括t统计量、p值和效应量度量在内的统计分析，突显了所提框架相对于原始贝叶斯优化的优越性。条件作用函数的自适应特性使算法能够无缝纳入新数据，使其在动态优化场景中尤其有益。
英文摘要：
This paper presents an innovative optimization framework and algorithm based on the Bayes theorem, featuring adaptive conditioning and jitter. The adaptive conditioning function dynamically modifies the mean objective function in each iteration, enhancing its adaptability. The mean function, representing the model's best estimate of the optimal value for the true objective function, is adjusted based on observed data. The framework also incorporates an adaptive acquisition jitter function, enhancing adaptability by adjusting the jitter of the acquisition function. It also introduces a robust objective function with a penalty term, aiming to generate robust solutions under uncertainty. The evaluation of the framework includes single-objective, decoupled multi-objective, and combined multi-objective functions. Statistical analyses, including t-statistics, p-values, and effect size measures, highlight the superiority of the proposed framework over the original Bayes optimization. The adaptive nature of the conditioning function allows the algorithm to seamlessly incorporate new data, making it particularly beneficial in dynamic optimization scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
