  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.
