摘要
昂贵优化问题的求解往往伴随着计算成本灾难,为了减少目标函数的真实评估次数,将序预测方法用于进化算法中候选解的选取.通过分类预测直接得到候选解的相对优劣关系,避免了对目标函数建立精确代理模型的需求,并且设计了序样本集约简方法,以降低序样本集的冗余性,提高序预测模型的训练效率.接下来,将序预测与遗传算法相结合.序预测辅助遗传算法在昂贵优化测试函数上的仿真实验表明,序预测方法可有效降低求解昂贵优化问题时的计算成本.
Solving expensive optimization problems is often accompanied by computational cost disasters.To reduce the number of real evaluations of the objective function,this study uses the ordinal prediction method in the selection of candidate solutions in evolutionary algorithms.The relative quality of candidate solutions is directly obtained through classification prediction,which avoids the need to establish an accurate surrogate model for the objective function.In addition,a reduction method for the ordinal sample set is designed to reduce the redundancy of the ordinal sample set and improve the training efficiency of the ordinal prediction model.Next,the ordinal prediction is combined with the genetic algorithm.The simulation experiments of the ordinal prediction-assisted genetic algorithm on the expensive optimization test function show that the ordinal prediction method can effectively reduce the computational cost of solving expensive optimization problems.
作者
毛立伟
贺慧芳
李文彬
郭观七
MAO Li-Wei;HE Hui-Fang;LI Wen-Bin;GUO Guan-Qi(School of Information Science and Engineering,Hunan Institute of Science and Technology,Yueyang 414006,China)
出处
《计算机系统应用》
2022年第11期199-206,共8页
Computer Systems & Applications
基金
湖南省教育厅科研项目(18A312)
湖南省教育厅优秀青年项目(19B231)
关键词
进化计算
昂贵优化
序预测
序样本集约简
evolutionary algorithm
expensive optimization
ordinal prediction
ordinal sample set reduction
作者简介
通信作者:李文彬,E-mail:wenbin_lii@163.com
