集成二级预测模型辅助的昂贵高维多目标进化算法

Ensemble Secondary Prediction Surrogate-assisted Expensive Multi-objective Evolutionary Algorithm

使用代理模型辅助进化算法能够提升昂贵优化问题的求解效率。然而,在处理昂贵高维多目标优化问题时,因目标维数较高且计算资源有限,使得代理模型建模困难,难以平衡收敛性和多样性的问题。为此,本研究提出了一种集成二级预测模型辅助的昂贵高维多目标进化算法(ESPSEMEA)。首先,将种群映射到径向空间和转置空间,然后使用克里金模型建立径向空间和转置空间的二级预测模型,使建模复杂度降低;另外,根据径向空间提供的多样性信息和转置空间提供的收敛性信息,将多样性和收敛性作为2个优化目标,使用非支配排序的方式选择后代,有效地平衡收敛性和多样性。通过与其他6个先进的算法进行对比分析,结果表明本文算法在降低建模时间复杂度的同时,能够有效地平衡收敛性和多样性。

The surrogate-assisted evolutionary algorithm can improve the solving efficiency of expensive optimization problems.When dealing with expensive multi-objective optimization problems,the number of objectives and the limitation of computational resources pose new challenges to the modeling of surrogate-assisted evolutionary algorithms and the balance between convergence and diversity.This paper proposes an ensemble secondary prediction surrogate-assisted expensive multi-objective evolutionary algorithm(ESPSEMEA).Firstly,the population is mapped to the transposed space and the radial space,respectively.Next,a two-level prediction model for radial space and transpose space is built by kriging model,which reduces the modeling complexity.In addition,according to the diversity information provided by the radial space and the convergence information provided by the transpose space,the diversity and convergence are taken as the two optimization goals,and the non-dominant sorting method is used to select the offspring,which can effectively balance the convergence and diversity.By comparing with the other six advanced algorithms,it is proved that the proposed algorithm can not only reduce the modeling time complexity but also effectively balance convergence and diversity.