[1] Brener N E, Looney H C, Sitharama Iyengar S, et al. Three-dimensional route planner using A* algorithm application to autonomous underwater vehicles[R].Baton Rouge, LA, US: Louisiana State University,2008. [2] 郝燕玲, 张京娟.基于遗传算法的AUV三维海底路径规划[J].中国工程科学,2003, 5(11):56-60. HAO Yan-ling, ZHANG Jing-juan. AUV path planning in 3D seabed environment using genetic algorithm[J]. Engineering Science, 2003, 5(11):56-60.(in Chinese) [3] 朱黎.基于改进蚁群算法的潜艇航路规划技术研究[D].长沙:国防科学技术大学,2009: 75-80. ZHU Li. Research of submarine route planning based on improved ant colony algorithm[D]. Changsha: National University of Defense Technology, 2009:75-80. (in Chinese) [4] 王奎民.水下潜器的航路规划技术综述[J].智能系统学报,2014,9(6):653-658. WANG Kui-min. The analysis on path planning for underwater vehicle[J].CAAI Transactions on Intelligent Systems, 2014,9(6):653-658. (in Chinese) [5] Dorigo M, Caro G D, Gambardella L M. Ant algorithms for discrete optimization [J].Artificial Life, 1999,5(3): 137-172. [6] 刘畅, 刘利强, 张丽娜,等. 改进萤火虫算法及其在全局优化问题中的应用[J]. 哈尔滨工程大学学报, 2017, 38(4):569-577. LIU Chang, LIU Li-qiang,ZHANG Li-na,et al. An improved firefly algorithm and its application in global optimization[J]. Journal of Harbin Engineering University, 2017,38(4):569-577. (in Chinese) [7] 徐玉如,姚耀中.考虑海流影响的水下机器人全局路径规划研究[J].中国造船,2008,49(4): 110-114. XU Yu-ru,YAO Yao-zhong. Research on AUV global path planning considering ocean current[J]. Shipbuilding of China, 2008,49(4): 110-114. (in Chinese) [8] 毛宇峰,庞永杰.改进粒子群在水下机器人路径规划中的应用[J]. 计算机应用,2010, 30(3):789-792. MAO Yu-feng, PANG Yong-jie. Application of improved particle swarm optimization in path planning of underwater vehicles[J]. Journal of Computer Applications, 2010, 30(3):789-792. (in Chinese) [9] 高博,徐得名.海流建模及其在路径规划中的应用[J].系统仿真学报,2010, 22(4):957-961. GAO Bo, XU De-ming. Method of designing optimal smooth way for vehicle [J]. Journal of System Simulation, 2010, 22(4):957-961. (in Chinese)
[10] 孙兆阳,邓晓刚.基于信息融合蚁群算法的机器人路径规划[J].工业控制计算机,2017,30(6): 26-31. SUN Zhao-yang, DENG Xiao-gang. Robot path planning based on information fusion ant colony algorithm[J]. Industrial Control Computer, 2017,30(6): 26-31. (in Chinese) [11] 郑慧君,陈俞强.基于改进蚁群的路径导航算法[J].控制工程,2016,23(4):608-612. ZHENG Hui-jun, CHEN Yu-qiang. Improved ACO-based path navigation algorithm[J]. Control Engineering of China, 2016,23(4): 608-612. (in Chinese) [12] Dorigo M, Maniezzo V, Colorni A. Ant system: optimization by a colony of cooperating agents[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 1996, 26(1): 29-41. [13] Gutjahr W J. A graph-based ant system and its convergence [J]. Future Generation Computer System, 2000,16(9):837-888. [14] Kolavali S R, Bhatnagar S. Ant colony optimization algorithms for shortest path problems[J]. Lecture Notes in Computer Science, 2008, 5425(1):37-44. [15] Glabowski M, Musznicki B,Nowak P, et al.An algorithm for finding shortest path tree using ant colony optimization metaheuristic[J]. Advances in Intelligent Systems and Computing, 2014, 233(5): 317-326. [16] Li Z,Gu W C,Zhang H L. Route planning of UAV based on hybrid of multi-population and adaptive ant colony algorithm[J].Computer Measurement & Control, 2015,23(5):1751-1753. [17] 张于贤, 丁修坤, 薛殿春,等. 求解旅行商问题的改进蚁群算法研究[J]. 计算机工程与科学, 2017, 39(8):1576-1580. ZHANG Yu-xian, DING Xiu-kun,XUE Dian-chun, et al. An improved ant colony algorithm for traveling salesman problem[J]. Computer Engineering & Science, 2017, 39(8): 1576-1580. (in Chinese)
第39卷 第9期2018 年9月兵工学报ACTA ARMAMENTARIIVol.39No.9Sep.2018
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