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浙江大学学报(工学版)
J4  2009, Vol. 43 Issue (11): 1951-1957    DOI: 10.3785/j.issn.1008-973X.2009.11.002
    
Ocean scientific survey route designing method syncretizing triangulated irregular networks and genetic algorithm
CHEN Hua-feng1, YE Shi-ping2, HUANG Zhi-cai1, ZHANG Xiao-can1
(1. Department of Earth Sciences, Zhejiang University, Hangzhou 310027, China;
2. College of Information, Zhejiang Shuren University, Hangzhou 310015, China)
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Abstract  

A method syncretizing triangulated irregular networks (TIN) and genetic algorithm (GA) was presented for designing ocean scientific survey route when precise methods could not be used because of low efficiency. The method first builds TIN from ports, work areas and inflexion points, then full searches all paths in TIN and makes the search result be part of GAs initial population to participate in the breeding, finally creates a route according to the anagenesis result. As closer points are linked as edges of triangles, paths in TIN should be comparatively the shortest of all possible paths because of its good adaptability to describe the neighborhood relationship of points. Therefore, GAs convergence is fast and the result is good with these paths being part of the initial population. Many simulation experiments showed that the TIN-GA method is more efficient than the full search method (FS) and can obtain better result than that of GA.

Published: 01 November 2009
CLC:  P 208  
  P 71  
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Cite this article:

CHEN Hua-Feng, XIE Shi-Beng, HUANG Zhi-Cai, et al. Ocean scientific survey route designing method syncretizing triangulated irregular networks and genetic algorithm. J4, 2009, 43(11): 1951-1957.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2009.11.002     OR     http://www.zjujournals.com/eng/Y2009/V43/I11/1951


融合不规则三角网和遗传算法的大洋科考航线设计方法

针对由于高耗时而无法使用精确方法的大洋科考航线设计问题,提出一种融合不规则三角网和遗传算法的航线设计方法（TIN-GA）.该方法由港口、作业区和拐点生成不规则三角网（TIN）,遍历搜索TIN中的所有路径,并将搜索获得的路径作为遗传算法初始路径种群的一部分参与优化繁殖,由进化结果生成航线.TIN将距离较近的点连接为三角形的边,具有很好的描述点邻近关系的自适应性,因此TIN中的路径必是所有可能路径中相对较优的路径.将这些路径作为初始路径种群的一部分能够加速遗传算法的收敛速度,并提高结果的最优性.大量仿真实验表明,该方法具有比遍历方法更高的效率,同时能够获得比遗传算法更优的结果.

[1] 任建国. 我国海洋科学“十一五”发展战略与优先资助领域[J]. 中国科学基金, 2007, 21(1): 7-13．
REN Jian-guo. Developmental strategy and funding preferences of “the eleventh five-year plan” of oceanography in China [J]. Bulletin of National Natural Science Foundation of China, 2007, 21(1): 7-13．
[2] 马良. 旅行推销员问题的算法综述[J]. 数学的实践与认识, 2000, 30(2): 156-165．
MA Liang. Algorithmic review on the travelling salesman problem [J]. Mathematics in Practice and Theory, 2000, 30(2): 156-165．
[3] 严晨,王直杰. 以TSP为代表的组合优化问题研究现状与展望[J]. 计算机仿真, 2007, 24(6): 171-174．
YAN Chen, WANG Zhi-jie. Study on combinatorial optimization problem represented by TSP: recent research work and perspective [J]. Computer Simulation, 2007, 24(6): 171-174．
[4] GLOVER F. Future paths for integer programming and links to artificial intelligence [J]. Computers & Operations Research, 1986, 13(5): 533-549．
[5] KIRKPATRICK S, GELATT C D, VECCHI M P. Optimization by simulated annealing [J]. Science, 1983, 220: 671-680．
[6] 刘宁钟,杨静宇. 遗传算法和Hopfield模型求解货郎担问题的比较和分析[J]. 计算机工程与应用, 2003,39(4): 95-97．
LIU Ning-zhong, YANG Jing-yu. Comparison and analyse between genetic algorithm and hopfield network to resolve TSP [J]. Computer Engineering and Applications, 2003,39(4): 95-97．
[7] RNDOLPH G. Convergence analysis of canonical genetic algorithms \[J\].IEEE Transactions on Neural Networks, 1994,5(1):96-101.
[8] 张晓缋，方浩，戴冠中.遗传算法的编码机制研究\[J\].信息与控制，1997,26(2):134-139.
ZHANG Xiao-hui, FANG Hao, DAI Guan-zhong. Study on encoding mechanism of genetic algorithms \[J\].Information and Control,1997,26(2):134-139.
[9] RNDOLPH G. Convergence analysis of canonical genetic algorithms [J]. IEEE Transactions on Neural Networks, 1994, 5(1): 96-101．
[10] BERTONI A, DORIGO M. Implicit parallelism in genetic algorithms \[J\].Artificial Intelligence，1993,61(2):307-314.
[11] SRINIVAS M, PATAIK L M. Genetic algorithms: a survey \[J\].IEEE Computer,1994,27(6):17-26.
[12] DORIGO M, GAMBARDELLA L M. Ant colony system: a cooperative learning approach to the traveling salesman problem [J]. IEEE Transactions on Evolutionary Computation, 1997,1(1): 53-66．
[13] POTVIN J Y, ROUSSEAUA J M. Parallel route building algorithm for the vehicle routing and scheduling problem with time windows [J]. European Journal of Operational Research, 1993, 66(3): 331-340．
[14] 邬伦,刘瑜,张晶,等. 地理信息系统：原理、方法和应用[M]. 北京：科学出版社, 2001:199-204.
[15] 武晓波,王世新,肖春生. 一种生成Delaunay三角网的合成算法[J]. 遥感学报, 2000, 4(1): 32-35．
WU Xiao-bo, WANG Shi-xin, XIAO Chun-sheng. A hybridized method for building Delaunay triangulation [J]. Journal of Remote Sensing, 2000, 4(1): 32-35．
[16] 章孝灿,黄智才,戴企成,等. GIS中基于拓扑结构和凸壳技术的快速TIN生成算法[J]. 计算机学报, 2002, 25(11): 1212-1218．
ZHANG Xiao-can, HUANG Zhi-cai, DAI Qi-cheng, et al. An algorithm of speedily building TIN based on topological structure and convex shell in GIS [J]. Chinese Journal of Computers, 2002, 25(11): 1212-1218.
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