[1]吴俊杰,纪卓尚,常会青.船体装配线划线路径规划的蚁群算法[J].哈尔滨工程大学学报,2012,(10):1205-1210.[doi:10.3969/j.issn.1006-7043. 201111059]
 WU Junjie,JI Zhuoshang,CHANG Huiqing.Ant colony algorithm for mark-line path planning[J].hebgcdxxb,2012,(10):1205-1210.[doi:10.3969/j.issn.1006-7043. 201111059]
点击复制

船体装配线划线路径规划的蚁群算法(/HTML)
分享到:

《哈尔滨工程大学学报》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2012年10期
页码:
1205-1210
栏目:
出版日期:
2012-10-25

文章信息/Info

Title:
Ant colony algorithm for mark-line path planning
文章编号:
1006-7043(2012)10-1205-06
作者:
吴俊杰 纪卓尚 常会青
1.大连理工大学 船舶CAD工程中心,辽宁 大连116024; 2.中远船务集团工程有限公司 技术中心,辽宁 大连 116113
Author(s):
WU Junjie JI Zhuoshang CHANG Huiqing
1. Ship CAD Engineering Center, Dalian University of Technology, Dalian116024, China; 2. Technical Center, COSCO Shipyard Group Company Ltd, Dalian 116113, China
关键词:
船体零件配线划线蚁群算法旅行商问题路径规划
分类号:
U671.99
DOI:
10.3969/j.issn.1006-7043. 201111059
文献标志码:
A
摘要:
船体零件装配线划线作业是与船体零件切割作业同时进行的,是现代造船模式中的一个重要环节.将船体零件划线路径规划问题作为广义旅行商问题进行分析,针对划线路径的特殊性,建立提出了改进的蚁群算法的路径规划模型,采用最大-最小蚁群算法进行优化,分析了算法中各参数取值对算法性能的影响,并同遗传算法作了比较.实验结果表明,基于蚁群算法的优化模型可以有效减少划线路径空走距离.实际应用表明可有效地减少作业时间,提高船厂生产效率.

参考文献/References:

[1]赵文彬, 孙志毅, 李虹. 一种求解TSP问题的相遇蚁群算法[J]. 计算机工程, 2004, 30(12): 136-138. ZHAO Wenbin, SUN Zhiyi, LI Hong. A meeting ant colony optimization algorithm of solving TSP problem [J]. Comuputer Engineering, 2004, 30(12): 136-138.
[2]房育栋, 郝建忠, 余英林,等. 遗传算法及其在TSP中的应用[J]. 华南理工学报, 1994, 22(3): 124-127. FANG Yudong, HAO Jianzhong, YU Yinglin, et al. Genetic algotithms and its application to TSP [J]. Journal of South China University of Technology, 1994, 22(3): 124-127.
[3]汪松泉, 程家兴. 遗传算法和模拟退火算法求解TSP的性能分析[J]. 计算机技术与发展, 2009, 19(11): 97-100. WANG Songquan, CHENG Jiaxing. Performance analysis on solving problem of TSP by genetic algorithm and simulated annealing [J]. Computer Technology and Development, 2009, 19(11): 97-100.
[4]赵学峰. 基于蚁群算法的一类扩展型TSP 研究[J]. 系统工程, 2003, 21 (1) : 17-21. ZHAO Xuefeng. Research on an extended TSP based-on ant colony algorithm[J]. Systems Engineering, 2003, 21 (1): 17-21.
[5]DORIGO M, MANIEZZO V, COLORNI A. The ant system: optimization by a colony of cooperating agents[J]. IEEE Tran s on Systems, Man, and Cybernetics-Part B, 1996, 26(1): 29-41.
[6]THOMAS S, HOOS H H. Max-min ant system [J]. Future Generation Computer Systems, 2000, 16(8): 889-914.
[7]DORIGO M, GAMBARDELLA L M. Ant colony system: a cooperative learning approach to the traveling salesman problem[J]. IEEE Trans on Evolutionary Computation, 1997, 1(1): 53-66.
[8]BONABEAU E, DORIGO M, THERAULAZ G. Inspiration for optimization from social insect behaviour [J]. Nature, 2000, 406 (6): 39-42.
[9]THOMAS S, DORIGO M. A short convergence proof for a class of ant colony optimization algorithms [J]. IEEE Trans on Evolutionary Computation, 2002, 6(4): 358-365.
[10]VERBEECK K, NOWE A. Colonies of learning automata [J]. IEEE Trans on Systems, Man, and Cybernetics—Part B, 2002, 32 (6): 772-780.
[11]钟珞, 赵先明, 夏红霞. 求解最小MPR集的蚁群算法与仿真[J]. 智能系统学报, 2011, 6(2): 166-171. ZHONG Luo, ZHAO Xianming, XIA Hongxia. An ant colony algorithm and simulation for solving minimum MPR sets[J]. CAAI Transactions on Intelligent Systems, 2011, 6(2): 166-171.
[12]印峰,王耀南,刘炜,等. 个体速度差异的蚁群算法设计及仿真[J].智能系统学报, 2009, 4(6): 528-533. YIN Feng, WANG Yaonan, LIU Wei, et al. Design and simulation of an ant colony algorithm based on individual velocity differences[J]. CAAI Transactions on Intelligent Systems, 2009, 4(6): 528-533.
[13]赵百轶,张利军,贾鹤鸣. 基于四叉树和改进蚁群算法的全局路径规划[J].应用科技, 2011, 38(10): 23-28. ZHAO Baiyi,ZHANG Lijun,JIA Heming. Global path planning based on quadtree and improved ant colony optimization algorithm[J]. Applied Science and Technology,2011, 38(10): 23-28.

更新日期/Last Update: 2012-11-14