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浏览/检索结果: 共7条，第1-7条 帮助 限定条件 专题：长春光学精密机械与物理研究所    第一署名单位    第一作者单位    通讯作者单位     已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 Chaotic Krill Herd algorithm 期刊论文 Information Sciences, 2014, 期号: 274, 页码: 17-34 Wang G. G.; Guo L. H.; Gandomi A. H.; Hao G. S.; Wang H. Q. 收藏  |  浏览/下载：39/0  |  提交时间：2015/04/24 Incorporating mutation scheme into krill herd algorithm for global numerical optimization 期刊论文 Neural Computing & Applications, 2014, 卷号: 24, 期号: 3-4, 页码: 853-871 Wang G. G.; Guo L. H.; Wang H. Q.; Duan H.; Liu L.; Li J. 收藏  |  浏览/下载：11/0  |  提交时间：2015/04/24 A new improved krill herd algorithm for global numerical optimization 期刊论文 Neurocomputing, 2014, 期号: 138, 页码: 392-402 Guo L. H.; Wang G. G.; Gandomi A. H.; Alavi A. H.; Duan H. 收藏  |  浏览/下载：20/0  |  提交时间：2015/04/24 A New Improved Firefly Algorithm for Global Numerical Optimization 期刊论文 Journal of Computational and Theoretical Nanoscience, 2014, 卷号: 11, 期号: 2, 页码: 477-485 Wang G. G.; Guo L. H.; Duan H.; Wang H. Q. 收藏  |  浏览/下载：18/0  |  提交时间：2015/04/24 Simulated Annealing-Based Krill Herd Algorithm for Global Optimization 期刊论文 Abstract and Applied Analysis, 2013, 卷号: 11 Wang G. G.; Guo L. H.; Gandomi A. H.; Alavi A. H.; Duan H. 收藏  |  浏览/下载：20/0  |  提交时间：2014/05/14 A hybrid meta-heuristic DE/CS Algorithm for UCAV path planning 期刊论文 Journal of Information and Computational Science, 2012, 卷号: 9, 期号: 16, 页码: 4811-4818 Wang G.; Guo L.; Duan H.; Liu L.; Wang H.; Wang J. 收藏  |  浏览/下载：47/0  |  提交时间：2013/03/27 Image matching using a bat algorithm with mutation (EI CONFERENCE) 会议论文 2012 International Conference on Mechatronic Systems and Automation Systems, MSAS 2012, July 21, 2012 - July 21, 2012, Wuhan, China Zhang J.; Wang G. 收藏  |  浏览/下载：29/0  |  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.