惯性分组和重叠特征选择辅助的昂贵大规模优化算法-《郑州大学学报(工学版)》

文章信息/Info

Title:: An Inertial Grouping and Overlapping Feature Selection Assisted Algorithm for Expensive Large-scale Optimization Problems

作者:: 邓传义¹ ; 孙超利² ; 刘晓彤² ; 张晓红³ ; 李春鹏⁴; 1. 太原科技大学应用科学学院,山西太原 030024;2. 太原科技大学计算机科学与技术学院,山西太原 030024; 3. 太原科技大学经济与管理学院山西太原 030024;4. 山西吉成科技股份有限公司山西太原 030000

Author(s):: DENG Chuanyi; SUN Chaoli; LIU Xiaotong; ZHANG Xiaohong; LI Chunpeng

Keywords:: large-scale optimization; expensive problems; overlapping feature selection; inertial grouping; surro; gate models; cooperative coevolutionar

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。

Abstract:: Challenges in expensive large-scale optimization problems, such as high coupling between variables, easy falling into local optimal solution, and computationally expensive objective function, resulted in the difficulty to achieve the global optimal solution. 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, good for solving problems with partially separable, overlapping or completely non-separable decision variables.