机构

• 自动化研究所 [2]
• 长春光学精密机械与物... [1]
• 沈阳自动化研究所 [1]

采集方式

• OAI收割 [4]

内容类型

• 期刊论文 [3]
• 会议论文 [1]

发表日期

• 2022 [1]
• 2020 [1]
• 2019 [1]
• 2012 [1]

学科主题

筛选

浏览/检索结果: 共4条，第1-4条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 Self-adaptive Bat Algorithm With Genetic Operations 期刊论文  OAI收割 IEEE/CAA Journal of Automatica Sinica, 2022, 卷号: 9, 期号: 7, 页码: 1284-1294 作者:  Jing Bi;  Haitao Yuan;  Jiahui Zhai;  MengChu Zhou;  H. Vincent Poor   |  收藏  |  浏览/下载：32/0  |  提交时间：2022/06/27 Bat algorithm (BA)  genetic algorithm (GA)  hybrid algorithm  learning mechanism  meta-heuristic optimization algorithms   A study of flexible flow shop scheduling problem with variable processing times based on improved bat algorithm 期刊论文  OAI收割 International Journal of Simulation and Process Modelling, 2020, 卷号: 15, 期号: 3, 页码: 245-254 作者:  Bian, Jianyong;  Yang LY(杨丽英)   |  收藏  |  浏览/下载：29/0  |  提交时间：2020/06/21 flexible flow shop  bat algorithm  BA  variable processing time  Hamming distance  adaptive position update   A Novel Bat Algorithm with Multiple Strategies Coupling for Numerical Optimization 期刊论文  OAI收割 MATHEMATICS, 2019, 卷号: 7, 期号: 2, 页码: 17 作者:  Wang, Yechuang;  Wang, Penghong;  Zhang, Jiangjiang;  Cui, Zhihua;  Cai, Xingjuan   |  收藏  |  浏览/下载：97/0  |  提交时间：2019/07/12 bat algorithm (BA)  bat algorithm with multiple strategy coupling (mixBA)  CEC2013 benchmarks  Wilcoxon test  Friedman test   Image matching using a bat algorithm with mutation (EI CONFERENCE) 会议论文  OAI收割 2012 International Conference on Mechatronic Systems and Automation Systems, MSAS 2012, July 21, 2012 - July 21, 2012, Wuhan, China 作者:  Zhang J.;  Wang G.;  Zhang J.;  Zhang J. 收藏  |  浏览/下载：38/0  |  提交时间：2013/03/25 Due to shortcoming of traditional image matching for computing the fitness for every pixel in the searching space  a new bat algorithm with mutation (BAM) is proposed to solve image matching problem  and a modification is applied to mutate between bats during the process of the new solutions updating. This new approach can accelerate the global convergence speed while preserving the strong robustness of the basic BA. The realization procedure for this improved meta-heuristic approach BAM is also presented. To prove the performance of this proposed meta-heuristic method  BAM is compared with BA and other population-based optimization methods  DE and SGA. The experiment shows that the proposed approach is more effective and feasible in image matching than the other model. (2012) Trans Tech Publications  Switzerland.