Surrogate-Assisted Expensive Many-Objective Optimization by Model Fusion
Surrogate-Assisted Expensive Many-Objective Optimization by Model Fusion
复制标题
DOI：
10.1109/cec.2019.8790155
复制
发表时间：
2019-06
期刊：
影响因子：
--
通讯作者：
Cheng He;Ran Cheng;Yaochu Jin;X. Yao
中科院分区：
文献类型：
--
作者：
Cheng He;Ran Cheng;Yaochu Jin;X. Yao
代理辅助进化算法在昂贵的优化中发挥了重要作用，其中允许少量的真实目标函数评估。通常，代理模型用于相同的目的，例如，以近似真实目标函数或聚合适应度函数。然而，通过模型融合进行代理辅助优化的工作很少，即，不同的代理模型被融合用于不同的目的，以提高算法的性能。在这项工作中，我们提出了一个代理人辅助的方法，通过模型融合解决昂贵的多目标优化问题，其中克里格辅助的目标函数近似方法与分类器辅助方法相融合。所提出的算法相比，一些国家的最先进的代理辅助算法DTLZ问题和现实世界的问题，我们提出的模型融合的方法已经取得了一些令人鼓舞的结果。
Surrogate-assisted evolutionary algorithms have played an important role in expensive optimization where a small number of real-objective function evaluations are allowed. Usually, the surrogate models are used for the same purpose, e.g., to approximate the real-objective function or the aggregation fitness function. However, there is little work on surrogate-assisted optimization by model fusion, i.e., different surrogate models are fused for different purposes to improve the performance of the algorithm. In this work, we propose a surrogate-assisted approach by model fusion for solving expensive many-objective optimization problems, in which the Kriging assisted objective function approximation method is fused with the classifier assisted approach. The proposed algorithm is compared with some state-of-the-art surrogate-assisted algorithms on DTLZ problems and a real-world problem, and some encouraging results have been achieved by our proposed model fusion based approach.