The proposed multi-objective optimization algorithm hybridizes random global search with a local refinement algorithm. The global search algorithm mimics the Bayesian multi-objective optimization algorithm. The site of current computation of the objective functions by the proposed algorithm is selected by randomized simulation of the bi-objective selection by the Bayesian-based algorithm. The advantage of the new algorithm is that it avoids the inner complexity of Bayesian algorithms. A version of the Hooke–Jeeves algorithm is adapted for the local refinement of the approximation of the Pareto front. The developed hybrid algorithm is tested under conditions previously applied to test other Bayesian algorithms so that performance could be compared. Other experiments were performed to assess the efficiency of the proposed algorithm under conditions where the previous versions of Bayesian algorithms were not appropriate because of the number of objectives and/or dimensionality of the decision space.

中文翻译：

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基于贝叶斯的全局搜索与 Hooke–Jeeves 局部细化的混合，用于多目标优化问题

所提出的多目标优化算法将随机全局搜索与局部细化算法相结合。全局搜索算法模仿贝叶斯多目标优化算法。通过基于贝叶斯算法的双目标选择的随机模拟来选择所提出算法对目标函数的当前计算的位置。新算法的优点是避免了贝叶斯算法的内在复杂性。Hooke-Jeeves 算法的一个版本适用于帕累托前沿近似的局部细化。开发的混合算法在先前用于测试其他贝叶斯算法的条件下进行了测试，以便可以比较性能。