结合特征扰动与分配策略的集成辅助多目标优化算法

代理模型利用近似预测代替算法对多目标优化问题的真实评价，大幅减少了算法寻优所需的真实适应度评估次数。为提高代理模型在求解高维问题时的准确性并降低计算开销，提出一种基于特征扰动与分配策略的集成辅助多目标优化算法。将径向基函数网络代理模型与支持向量机回归代理模型作为集成过程中的基模型，降低算法在高维问题上的计算开销。结合特征扰动与基于记忆的影响因子分配策略构建集成代理模型，提高集成准确性。使用集成预测值与不确定信息加权辅助管理集成代理模型，平衡全局搜索与局部探索，增强算法在目标空间中的寻优能力。实验结果表明，该算法在ZDT1~ZDT3和ZDT6测试问题上所得解集的分布性与收敛性相比经典算法更好，并且当决策变量维数增加时，使用集成代理模型相比于Kriging代理模型约减少了90%的适应度评估次数，同时可获得更准确的预测结果。

Ensemble-Assisted Multi-Objective Optimization Algorithm Combining Feature Perturbation and Allocation Strategy

A surrogate model can use its approximate prediction to replace the real evaluation of an algorithm when applied to the multi-objective optimization problem, which greatly reduces the number of real fitness evaluations required by the multi-objective optimization algorithm for determining the optimal optimization algorithm.To improve the accuracy of the surrogate model under high-dimensional problems and reduce its computational overhead, this study proposes an ensemble-assisted multi-objective optimization algorithm based on feature perturbation and allocation strategy.First, the algorithm uses a Radial Basis Function Network(RBFN) and a Support Vector Regression(SVR) surrogate model as the basis model during the integration, which reduces the computational cost of the algorithm on high-dimensional problems.Second, the algorithm combines feature disturbance and memory-based influence factor allocation strategies to construct an integrated surrogate model and improve the accuracy of the integration.Finally, the algorithm uses the integrated prediction value and the weighted uncertainty information to assist in the management of the integrated surrogate model, balance the global search and local exploration, and enhance the optimization capability of the algorithm within the target space.The experimental results show that the distribution and convergence of the solution set obtained by this algorithm on the ZDT1~ZDT3 and ZDT6 test problems are better than those of a classical algorithm.In addition, when the number of dimensions of the decision variables increases, the ensemble surrogate model used by the algorithm reduces the number of fitness evaluations by approximately 90% in comparison with the Kriging surrogate model, and at the same time can obtain more accurate prediction results.