为使锚泊系统能根据船舶使用环境,合理地调整锚链张力来保证船舶作业安全和定位要求,其张力的优化分配是必要的。在研究锚链张力优化模型和遗传算法基础上,针对遗传算法早熟收敛,后期搜索迟钝及多样性保持不好的缺点,采用个体相似度交叉配对策略,改进交叉、变异自适应操作、非线性规划;量子化编码,旋转门动态调整及自适应量子变异、灾变的措施改进遗传算法,并将改进算法应用于1 000 t应急打捞起重船锚泊定位系统张力优化中,仿真结果及性能分析表明该方法全局搜索能力和收敛性能明显提高。验证了改进张力分配算法的合理性和有效性。
In order to make the mooring system reasonably adjust the tensions to ensure the operation of ship's safety and positioning requirements according to environment conditions, the tension optimization allocation is necessary. In this paper, tension optimization model of chains and genetic algorithm theory are studied. To overcome the genetic algorithm's premature convergence, slow convergence speed and the bad population diversity the measures of cross matching strategy of individual similarity, adaptive adjustment of crossover and mutation probability, nonlinear programming; quantum encoding, rotation gate dynamic adjustment, adaptive quantum mutation and catastrophe are adopted to improve algorithms. Finally, the improved algorithms are applied to simulate in tension optimization of a 1 000 t emergency salvage crane ship's mooring system, the results show that the methods' global optimum capability and convergence rate are obviously improved, and verify the tension optimizing allocation algorithms that are reasonable and effective.
2018,40(3): 66-70 收稿日期:2016-11-09
DOI:10.3404/j.issn.1672-7649.2018.03.012
分类号:TP29
基金项目:江苏省产学研联合创新资金资助项目(BY2013066-08);江苏科技大学海洋装备研究院科研基金资助项目(HZ2015006)
作者简介:李业(1991-),男,硕士研究生,研究方向为船舶自动化、计算机应用
参考文献:
[1] BERNTSEN P I B, AAMOOM, LEIRA B J. Ensuring mooring line integrity by dynamic positioning:controller design and experimental tests[J]. Automatic, 2009, 45(5):1285-1290.
[2] 王雪, 陆晶. 船舶操控及锚泊过程的运动数学建模研究[J]. 舰船科学技术, 2016, 06:13-15.WANG Xue, LU Jing. Study on mathematical modeling of ship motion control and mooring process[J]. Ship Science and Technology, 2016, 06:13-15.
[3] JI S W, CHOI M S, KIM Y B. A study on position mooring system design for the vessel moored by mooring lines[J]. IEEE/ASME Transactions on Mechatronics, 2015, 20(6):2824-2831.
[4] CHOUBEY N S, KHARAT M U. Approaches for handling premature convergence in CFG induction using GA[J]. Soft Computing in Industrial Application, 2011, 96:55-56.
[5] SERKAN BEKIROGLU, TAYFUN DEDE, YUSUF AYVAZ. Implementation of different encoding types on structural optimization based on adaptive genetic algorithm[J]. Finite Elements in Analysis and Design, 2009, 45(11):826-835.
[6] 李小青, 张文祥. 基于退火免疫遗传算法的测试用例生成研究[J]. 计算机仿真, 2008, 25(5):171-174.LI Xiao-qing, ZHANG Wen-xiang. Test case generation based on annealing immune genetic algorithm[J]. Computer Simulation, 2008, 25(5):171-174.
[7] 任子武, 伞冶. 自适应遗传算法的改进及在系统辨识中的应用研究[J]. 系统仿真学报, 2006, 18(1):41-6.REN Zi-wu, SANYe. Improved adaptive genetic algorithm and its application research in parameter identification[J]. Journal of System Simulation, 2006, 18(1):41-6.
[8] 齐鸣, 严传续, 孟宏斌, 等. 船舶锚泊系统扰动力平衡计算[J]. 中国造船, 2012, 53(3):158-164.QI Ming, YAN Chuan-xu, MENG Hong-bin, et al. Balance calculation on disturbing force of mooring system for vessel[J]. Shipbuilding of China, 2012, 53(3):158-164.
[9] SHAFIEEFAR M, REZVANI A. Mooring optimization of floating platforms using a genetic algorithm[J]. Ocean Engineering, 2007, 34(10):1413-1421.
[10] 金鸿章, 苏晓宇, 于安才, 等. 基于锚链切换的平台自动锚泊定位系统设计[J]. 电机与控制学报, 2014, 18(5):93-98.JIN Hong-zhang, SU Xiao-yu, YU An-cai, et al. Design of automatic mooring positioning system based on mooring line switch[J]. Electric Machines and Control, 2014, 18(5):93-98.
[11] 严传续, 钱宏, 项军毅, 等. 铺管船锚泊定位系统优化设计研究[J]. 中国造船, 2010, 51(1):83-93.YAN Chuan-xu, QIAN Hong, XIANG Jun-yi, et al. Study on design optimization of mooring positioning system for pipe laying vessel[J]. Shipbuilding of China, 2010, 51(1):83-93.
[12] 田小梅, 郑金华, 李合军. 基于父个体相似度的自适应遗传算法[J]. 计算机工程与应用, 2005, 18(3):61-63.TIAN Xiao-mei, ZHENG Jin-hua, LI He-jun. Adaptive genetic algorithm based on parents' similarity[J]. Computer Engineering and Applications, 2005, 18(3):61-63.
[13] 刘建文, 丁洁玉, 潘坤, 等. 基于个体相似度的改进自适应遗传算法研究[J]. 青岛大学学报(工程技术版), 2016, 31(1):16-19.LIU Jian-wen, DING Jie-yu, PAN Kun, et al. Improved adaptive genetic algorithm based on individual similarity[J]. Journal of Qingdao University(E&T), 2016, 31(1):16-19.
[14] 陈超. 自适应遗传算法的改进研究及其应用[D]. 广州:华南理工大学, 2011.
[15] 胡宽, 常新龙, 宋笔锋, 等. 求解含等式约束化问题的遗传算法[J]. 上海交通大学学报, 2011, 45(1):92-97.HU Kuan, CHANG Xin-long, SONG Bi-feng, et al. Genetic algorithm to solve optimization problem with equality constrains[J]. Journal of Shanghai Jiao Tong University, 2011, 45(1):92-97.
[16] HAN Kuk-Hyun, KIM Jong-Hwan. On the analysis of the quantum-inspired evolutiongary algorithm with a single individual[C]//IEEE Congress on Evolutionary Compution Shetaton Vancouver Wall Centre Hotel, Vancouver, BC, Canada, 2006:9172.