| 摘要: |
| 提出了一个基于多样性分类和距离回归的进化算法(DCDREA),以解决昂贵超多目标优化问题(MaOPs).DCDREA采用随机森林(RF)作为全局分类代理模型,它把种群中所有解作为训练样本,并根据是否为最小相关解,把训练样本分类为正负样本,使模型学习到训练样本中含有的分类标准.全局分类代理模型主要用来筛选新产生的候选解,以得到一组有希望的候选解.此外,它采用Kriging作为局部回归代理模型,其选择种群中距离当前候选解最近的解作为训练样本,拟合训练样本与理想点的距离.然后,通过K-means方法把候选解划分为μ个簇,并从每个簇中选择一个用于真实评估的候选解.在实验部分,使用大规模3、4、6、8、10目标的DTLZ测试问题集,把DCDREA与目前流行的代理辅助进化算法进行对比实验.对于不同测试问题,每个算法独立运行20次,然后统计反向迭代距离(IGD)和算法运行时间.最后,使用秩和检验来分析结果.实验对比结果表明,DCDREA在大多数情况下表现更好.由此可见,DCDREA具有较好的有效性和可行性. |
| 关键词: 代理模型 进化算法 超多目标优化 昂贵问题 随机森林 Kriging |
| DOI：10.13328/j.cnki.jos.006310 |
| 分类号:TP301 |
| 基金项目:江苏省科技支撑计划(BE2013879) |
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| Diversity Classification and Distance Regression Assisted Evolutionary Algorithm |
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SUN Zhe-Ren, HUANG Yu-Hua, CHEN Zhi-Yuan
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College of Computer Science and Technology (Nanjing University of Aeronautics and Astronautics), Nanjing 211106, China
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| Abstract: |
| This study proposed a diversity classification and distance regression assisted evolutionary algorithm (DCDREA) to solve expensive many-objective optimization problems (MaOPs). In DCDREA, the random forest (RF) is adopted as the global classification surrogate model. All the solutions in the population are as the training samples and classified into positive or negative samples according to whether they are minimum correlative solutions, so that the model can learn the classification criteria contained in the training samples. The global classification surrogate model is mainly used to filter the newly generated candidates to obtain a group of promising candidates. In addition, Kriging is adopted as the local regression surrogate model, where the solutions closest to the current candidates in the population are selected as the training samples, and the distance between the training samples and the ideal point is approximated by the model. Then, by the K-means method, the candidates are divided into μ clusters, and from each cluster, one candidate is selected for real function evaluation. In the experimental part, the DTLZ suite with large scale 3, 4, 6, 8, and 10 objectives was used to compare DCDREA with the current popular surrogate-assisted evolutionary algorithms. For different test problems, each algorithm was run independently for 20 times. Then the inverted generational distance (IGD) and algorithm running time were counted. At last, the Wilcoxon rank sum test was used to analyze the results. The result comparison shows that DCDREA performs better in most cases, indicating that DCDREA has sound effectiveness and feasibility. |
| Key words: surrogate evolutionary algorithm many-objective optimization expensive problem random forest Kriging |
