期刊文献+
共找到3篇文章
< 1 >
每页显示 20 50 100
Improved genetic algorithm freely searching for dangerous slip surface of slope 被引量:4
1
作者 万文 曹平 +1 位作者 冯涛 袁海平 《Journal of Central South University of Technology》 EI 2005年第6期749-752,共4页
Based on the slice method of the non-circular slip surface for the calculation of integral stability of slope, an improved genetic algorithm was proposed, which can freely search for the most dangerous slip surface of... Based on the slice method of the non-circular slip surface for the calculation of integral stability of slope, an improved genetic algorithm was proposed, which can freely search for the most dangerous slip surface of slope and the corresponding minimum safety factor without supposing the geometric shape of the most dangerous slip surface. This improved genetic algorithm can simulate the genetic evolution process of organisms and avoid the local minimum value compared with the classical methods. The results of engineering cases show that it is a global optimal algorithm and has many advantages, such as higher efficiency and shorter time than the simple genetic algorithm. 展开更多
关键词 slice method dangerous non-circular slip surface minimum safety factor improved genetic algorithm
在线阅读 下载PDF
考虑交货期的双资源柔性作业车间节能调度 被引量:9
2
作者 张洪亮 徐静茹 +1 位作者 谈波 徐公杰 《系统仿真学报》 CAS CSCD 北大核心 2023年第4期734-746,共13页
为解决含有机器和工人双资源约束的柔性作业车间节能调度问题,在考虑交货期的基础上,建立了以总提前和拖期惩罚值及总能耗最小为目标的双资源柔性作业车间节能调度模型。提出了一种改进的非支配排序遗传算法(improved non-dominated sor... 为解决含有机器和工人双资源约束的柔性作业车间节能调度问题,在考虑交货期的基础上,建立了以总提前和拖期惩罚值及总能耗最小为目标的双资源柔性作业车间节能调度模型。提出了一种改进的非支配排序遗传算法(improved non-dominated sorting genetic algorithmⅡ,INSGA-Ⅱ)进行求解。针对所优化的目标,设计了一种三阶段解码方法以获得高质量的可行解;利用动态自适应交叉和变异算子以获得更多优良个体;改进拥挤距离以获得收敛性和分布性更优的种群。将INSGA-Ⅱ与多种多目标优化算法进行对比分析,实验结果表明所提算法可行且有效。 展开更多
关键词 双资源约束 柔性作业车间 提前/拖期惩罚 能耗 INSGA-Ⅱ(improved non-dominated sorting genetic algorithmⅡ)
在线阅读 下载PDF
Knowledge-based detection method for SAR targets
3
作者 Fei Gao Achang Ru +1 位作者 Jun Wang Shiyi Mao 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2014年第4期573-579,共7页
When the classical constant false-alarm rate (CFAR) combined with fuzzy C-means (FCM) algorithm is applied to target detection in synthetic aperture radar (SAR) images with complex background, CFAR requires bloc... When the classical constant false-alarm rate (CFAR) combined with fuzzy C-means (FCM) algorithm is applied to target detection in synthetic aperture radar (SAR) images with complex background, CFAR requires block-by-block estimation of clutter models and FCM clustering converges to local optimum. To address these problems, this paper pro-poses a new detection algorithm: knowledge-based combined with improved genetic algorithm-fuzzy C-means (GA-FCM) algorithm. Firstly, the algorithm takes target region's maximum and average intensity, area, length of long axis and long-to-short axis ratio of the external ellipse as factors which influence the target appearing probabil- ity. The knowledge-based detection algorithm can produce preprocess results without the need of estimation of clutter models as CFAR does. Afterward the GA-FCM algorithm is improved to cluster pre-process results. It has advantages of incorporating global optimizing ability of GA and local optimizing ability of FCM, which will further eliminate false alarms and get better results. The effectiveness of the proposed technique is experimentally validated with real SAR images. 展开更多
关键词 synthetic aperture radar (SAR) target detection knowledge-based improved genetic algorithm-fuzzy C-means(GA-FCM) algorithm.
在线阅读 下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部