摘要
昂贵大规模优化问题存在着决策变量之间高度耦合、求解容易陷入局部最优以及目标评价昂贵等问题,导致在资源有限的情况下很难获得全局最优解。为此,基于合作型协同演化策略提出了一种惯性分组和重叠特征选择的方法来辅助求解昂贵大规模优化问题。首先,采用重叠特征选择技术将一个大规模优化问题分解为若干个低维的重叠子问题,并对每一个子问题进行独立的代理模型辅助的优化搜索。其次,将每个子问题搜索获得的潜力个体合成一个完整的解,对其使用昂贵目标函数进行评价。再次,算法还采用惯性分组技术控制优化过程中重新分组的频率以延长分组方案的开发周期,从而提升优化效果。最后,为了测试所提算法的性能,将其与求解昂贵大规模问题的3种优化算法在CEC2013的15个基准函数上获得的实验结果进行了对比。结果表明:所提算法在求解昂贵大规模优化问题上具有一定的竞争力,尤其适用于求解部分可分离、重叠或完全不可分离等问题。
Challenges in expensive large-scale optimization problems,such as high coupling between variables,easy falling into local optimal solution,and computationally expensive objective function,which resulted in the difficulty to achieve the global optimal solution.This paper An inertial grouping and overlapping feature selection technique for cooperative coevolutionary(IG-OFSA)algorithms was proposed to solve expensive large-scale optimization problems.In the proposed algorithm,firstly,a large-scale optimization problem was decomposed into several low-dimensional overlapping sub-problems by using overlapping feature selection technology,and each sub-problem was optimized independently with the assistance of a surrogate model.Then,promising solutions found for each sub-problem would be merged into a context vector for expensive objective evaluation.In addition,an inertial grouping technology was used to control the frequency of regrouping during the optimization to extend the cycle of exploitation of the grouping scheme,and correspondingly improved the performance of optimization.The performance of IG-OFSA was tested on 15 CEC2013 benchmark problems and compared with three state-of-the-art algorithms.The experimental results showed that the performance of IG-OFSA was competitive to solve the expensive large-scale optimization problem,especially,it was good for solving problems with partially separable,overlapping or completely non-separable decision variables.
作者
邓传义
孙超利
刘晓彤
张晓红
李春鹏
DENG Chuanyi;SUN Chaoli;LIU Xiaotong;ZHANG Xiaohong;LI Chunpeng(School of Applied Science,Taiyuan University of Science and Technology,Taiyuan 030024,China;School of Computer Science and Technology,Taiyuan University of Science and Technology,Taiyuan 030024,China;School of Economics and Management,Taiyuan University of Science and Technology,Taiyuan 030024,China;Shanxi Jicheng Technology Co.,Ltd.,Taiyuan 030000,China)
出处
《郑州大学学报（工学版）》
CAS
北大核心
2023年第5期32-39,共8页
Journal of Zhengzhou University（Engineering Science）
基金
国家自然科学基金资助项目(61876123)
山西省重点研发计划项目(202102020101002)
山西省自然科学基金资助项目(202203021211194)
关键词
大规模优化
昂贵问题
重叠特征选择
惯性分组
代理模型
合作型协同演化
large-scale global optimization
expensive problems
overlapping feature selection
inertial grouping
surrogate models
cooperative coevolutionary
