期刊文献+

基于HPSO算法和GA的舰载机甲板布放方法比较 被引量:7

Comparison of deck-disposed methods for shipboard aircraft based on HPSO and GA
下载PDF
导出
摘要 以戴高乐航母为研究对象,基于不同优化算法,对其舰面舰载机布放问题的解决方法进行比较,以此作为解决其他类型航母同样问题的参考。首先,分析了解决舰载机舰面布放调度问题的先决条件,包括舰面战位的设置;各战位间距离的测量计算;舰载机正常的出动流程分析;舰载机出动时间计算公式的设计。其次,将舰载机舰面布放调度问题转换为带有约束条件的多目标函数求最小解问题,并给出了数学模型。再次,给出了利用改进的粒子群优化(honeybee particle swarm optimization,HPSO)算法和遗传算法(genetic algorithm,GA)对问题求解的解决思路。最后,对两种算法50次独立运算的结果,分别从平均最短出动时间、平均最短移动距离、标准偏差以及算法的收敛性和精确性等方面进行比较。结果表明,HPSO算法较GA更适合于解决该布放问题。 The deck-disposed scheduling methods of carrier planes about De Gaulle are researched based on different optimization algorithms, which can be used to solve the same questions of another aircraft carrier. First, the basic conditions of deck-disposed scheduling problem of carrier planes are analysed, which includes battle position setting, distance measurement between gate position and preparative position, natural takeoff flow analysis, takeoff time expressions about different numbers of carrier planes. Second, the deck-disposed scheduling question is changed into a muhiobjective functions with restriction which needs to figure out the mini mum solution, then the mathematical model is proposed. Third, the honeybee particle swarm optimization (HPSO) and genetic algorithm (GA) are used to solve the problem separately. In the end, the average shortest take- off time, average shortest moving distance, standard deviation and the astringency and accuracy of algorithms about fif- ty separate operations are compared. The result shows that HPSO is fitter than GA for solving the problem.
出处 《系统工程与电子技术》 EI CSCD 北大核心 2013年第2期338-344,共7页 Systems Engineering and Electronics
基金 中国博士后科学基金(201003758)资助课题
关键词 甲板布放 改进的粒子群优化算法 遗传算法 deck-disposition honeybee particle swarm optimization (HPSO) genetic algorithm (GA)
  • 相关文献

参考文献17

  • 1李鸣,王英杰,吕杰.国外舰载机琶行员培训[M].北京:航夺工业出版社,2009:110.
  • 2韩维,王庆官.航母与舰载机概论[M].烟台:海军航空工程学院出版社,2009:37-41.
  • 3NAVA1R 00 - 80T - 120. CV Flight / Hangar deck NATOPS manual[S]. Washington D. C. : Authority of the Chief of Naval Operations, 200l.
  • 4李鸣,王英杰,吕杰.21世纪美国舰载航空力量[M].北京:航空工业出版社,2009:5967.
  • 5Johnston J S. A feasibility study of a persistent monitoring sys- tem for the flight deck of U. S. Navy Aircraft Carriers[D]. Cali fornia.. Air University, 2009.
  • 6陈书海,张正满.航空母舰海军史上的里程碑[M].北京:国防工业出版社,2007:43-67.
  • 7Yen G G, I.eong W F. Dynamic multiple swarms in multiobjec tire particle swarm optimization[J]. IEEE Trans. on Systems, Man, and Cybernetics Part A Systems and Humans, 2009,39(4):890-911.
  • 8司维超,韩维,史玮韦.一种基于蜜蜂多群体觅食的粒子群优化算法.第三十届中国控制会议论文集.北京:中国科学院数学与系统科学研究院,2011.
  • 9Serkan H, Turker I, Alper Y. Fractional particle swarm optimi- zation in multidimensional search space[J]. IEEE Trans. on Systems, Man, and Cybernetics- Part B Cybernetics, 2010, 40(2) .-298 - 318.
  • 10Serkan H, Turker I, Alper Y. Fractional particle swarm optimiJ zation in multidimensional search space[J]. IEEE Trans. o Systems, Man, and Cybernetics- Part B: Cybernetics, 20101 40(2) :298 - 318. /.

二级参考文献1

共引文献13

同被引文献76

引证文献7

二级引证文献57

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部