基于估值不确定度排序顺序均值采样的昂贵高维多目标进化算法

Sampling based on mean value of ranking on approximation uncertainties for expensive many-objective evolutionary algorithm

模型管理,特别是训练样本的选择和填充采样准则,是影响昂贵多目标优化算法求解性能的重要因素.为此,选择样本库中具有较好目标函数值的若干个体作为样本训练目标函数的代理模型,使用基于参考向量的进化算法搜索模型的最优解集,并提出一种基于个体目标函数估值不确定度排序顺序均值的采样策略,从该最优解集中选择两个个体进行真实的目标函数评价.为了验证算法的有效性,将所提出算法在DTLZ和WFG多目标优化测试问题和两个实际工程优化问题上进行测试,并与其他5种优秀的同类型算法进行结果对比.实验结果表明,所提出算法在求解昂贵高维多目标优化问题上是有效的.

Model management,especially the selection of samples for model training and the infill sampling criterion,plays a significant role in the expensive many-objective optimization.Therefore,a number of good solutions will be selected from the database and used as samples to train surrogate models of objectives.The optimal non-dominated solutions of the surrogate models will be searched by the reference vector based evolutionary algorithm,among which two solutions will be selected for exact objective evaluation.The sampling criterion is proposed based on the mean value of the ranking on the approximation uncertainty(SaMVRAU).To evaluate the performance of the proposed method,a number of experiments are conducted on DTLZ and WFG many-objective optimization test problems,as well as two real-world applications.The experimental results show that the proposed method is efficient to solve expensive many-objective problems.