Wuxi Institute of Technology, Wuxi, Jiangsu 214121, China.
Jiangnan University, Wuxi, Jiangsu 214122, China.
Comput Intell Neurosci. 2022 Jun 25;2022:5227975. doi: 10.1155/2022/5227975. eCollection 2022.
Although the integrated model has good convergence ability, it is difficult to solve the multimodal problem and noisy problem due to the lack of uncertainty evaluation. Radial basis function model performs best for different degrees of nonlinear problems with small-scale and noisy training datasets but is insensitive to the increase of decision-space dimension, while Gaussian process regression model can provide prediction fitness and uncertainty evaluation. Therefore, an adaptive weighted strategy based integrated surrogate models is proposed to solve noisy multiobjective evolutionary problems. Based on the indicator-based multiobjective evolutionary framework, our proposed algorithm introduces the weighted combination of radial basis function and Gaussian process regression, and U-learning sampling scheme is adopted to improve the performance of population in convergence and diversity and judge the improvement of convergence and diversity. Finally, the effectiveness of the proposed algorithm is verified by 12 benchmark test problems, which are applied to the hybrid optimization problem on the construction of samples and the determination of parameters. The experimental results show that our proposed method is feasible and effective.
尽管集成模型具有良好的收敛能力，但由于缺乏不确定性评估，因此很难解决多峰问题和噪声问题。径向基函数模型在处理小规模和噪声训练数据集的不同程度非线性问题时表现最佳，但对决策空间维度的增加不敏感，而高斯过程回归模型可以提供预测拟合度和不确定性评估。因此，提出了一种基于自适应加权策略的集成代理模型，以解决噪声多目标进化问题。基于基于指标的多目标进化框架，我们的算法引入了径向基函数和高斯过程回归的加权组合，并采用 U 学习抽样方案来提高种群在收敛性和多样性方面的性能，并判断收敛性和多样性的提高。最后，通过 12 个基准测试问题验证了所提出算法的有效性，并将其应用于样本构建和参数确定的混合优化问题。实验结果表明，所提出的方法是可行和有效的。
