基于剪枝堆栈泛化的离线数据驱动进化优化

Offline Data Driven Evolutionary Optimization Based on Pruning Stacked Generalization

现实世界中存在很多目标函数的计算非常昂贵,甚至目标函数难以建模的复杂优化问题.常规优化方法在解决此类问题时要么无从入手,要么效率低下.离线数据驱动的进化优化方法不需对真实目标函数进行评估,跳出了传统优化方法的固铚,极大推动了昂贵优化问题和不可建模优化问题的求解.但离线数据驱动进化优化的效果严重依赖于所采用代理模型的质量.为提升离线数据驱动进化优化的性能,提出了一个基于剪枝堆栈泛化(Stacked generalization,SG)代理模型构建方法.具体而言,一方面基于异构的基学习器建立初级模型池,再采用学习方式对各初级模型进行组合,以提升代理模型的通用性和准确率.另一方面基于等级保护指标对初级模型进行剪枝,在提高初级模型集成效率的同时进一步提升最终代理模型的准确率,并更好地指导种群的搜索.为验证所提方法的有效性,与7个最新的离线数据驱动的进化优化算法在12个基准测试问题上进行对比,实验结果表明所提出的方法具有明显的优势.

In the real world,there are many complex optimization problems in which the objective function is timeconsuming or even unavailable.Traditional optimization methods are either unable to start or inefficient in solving such problems.Offline data driven evolutionary optimization method is no need to evaluate the real objective function in the process of evolutionary,which greatly promotes the solution of expensive optimization problems and unmodeled optimization problems.However,the effectiveness of offline data driven evolutionary optimization depends heavily on surrogate model.In order to improve the quality of surrogate model,this paper proposes a surrogate model construction method based on pruning stack generalization(SG).Specifically,on the one hand,the primary model pool is established based on heterogeneous base learners,and then the primary models are conducted by learning methods to improve the generality and accuracy of surrogate model.On the other hand,the primary models are pruned based on the ranking preservation indicator,which can not only improve the ensemble efficiency of the primary models,but also further improve the accuracy of the final surrogate model,and better guide the evolutionary search.In order to verify the effectiveness of the proposed method,it is compared with 7 state-of-the-arts offline data driven evolutionary optimization algorithms on 12 benchmark problems.The experimental results demonstrate the superior performance of proposed method over compared algorithms.