基于IPSO的舰载机出动离场规划研究

doi:10.12305/j.issn.1001-506X.2024.04.22

摘要/Abstract

摘要：

针对舰载机机群出动离场效率问题, 提出了一种改进的粒子群优化(improved particle swarm optimization, IPSO)算法。首先，基于舰载机的出动离场流程及约束建立了舰载机出动离场数学模型，并根据出动离场问题特点设计了一种连续编码形式以表示出动规划方案。然后, 通过在参数自适应粒子群优化算法的基础上引入基于莱维飞行扰动的模拟退火机制, 提出了将IPSO用于对出动离场规划问题进行求解。最后, 分别针对8、10、12机的出动离场规划场景进行了仿真验证。仿真结果表明, 所提算法较已有的优化算法而言具有更好的寻优能力。

关键词: 舰载机, 出动离场, 粒子群优化, 莱维飞行, 模拟退火

Abstract:

An improved particle swarm optimization (IPSO) algorithm is proposed to address the problem of departure efficiency for carrier aircraft. Firstly, a mathematical model for the departure process and constraints of carrier aircraft is established, and a continuous coding form is designed to represent the departure planning scheme based on the characteristics of the departure problem. Then, by introducing a simulated annealing mechanism based on Levy flight disturbance on the basis of parameter adaptive particle swarm optimization algorithm, the IPSO algorithm is proposed for solving the departure planning problem. Finally, simulation verification is conducted for the departure planning scenarios of aircraft 8, 10, and 12 respectively. The simulation results indicate that the proposed algorithm has better optimization ability compared to existing optimization algorithms.

Key words: carrier aircraft, departure, particle swarm optimization (PSO), Levy flight, simulated annealing

中图分类号:

V355.2

刘玉杰, 崔凯凯, 韩维, 李樾. 基于IPSO的舰载机出动离场规划研究[J]. 系统工程与电子技术, 2024, 46(4): 1337-1345.

Yujie LIU, Kaikai CUI, Wei HAN, Yue LI. Research on departure planning of carrier aircraft based on IPSO[J]. Systems Engineering and Electronics, 2024, 46(4): 1337-1345.

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参考文献 30

1	刘玉杰, 万兵, 苏析超, 等. 基于IABC算法的舰载机着舰调度[J]. 控制与决策, 2022, 37 (7): 1810- 1818.
	LIU Y J , WAN B , SU X C , et al. Scheduling of landing for carrier-based aircraft based on improved artificial bee colony algorithm[J]. Control and Decision, 2022, 37 (7): 1810- 1818.
2	LIU Y J , HAN W , SU X C , et al. Optimization of fixed aviation support resource station configuration for aircraft carrier based on aircraft dispatch mission scheduling[J]. Chinese Journal of Aeronautics, 2023, 36 (2): 127- 138. doi: 10.1016/j.cja.2022.06.023
3	司维超, 韩维, 宋岩. 面向任务T-Petri网的舰载机出动流程仿真[J]. 计算机工程与应用, 2014, 50 (16): 237- 242.
	SI W C , HAN W , SONG Y , et al. Takeoff procedure simulation of carrier plane based on T-Petri[J]. Computer Engineering and Applications, 2014, 50 (16): 237- 242.
4	司维超, 韩维, 史玮韦. 基于PSO算法的舰载机舰面布放调度方法研究[J]. 航空学报, 2012, 33 (11): 2048- 2058.
	SI W C , HAN W , SHI W W , et al. Research on deck-disposed scheduling method of carrier planes based on PSO algorithm[J]. Acta Aeronautica et AstronatuticaSinica, 2012, 33 (11): 2048- 2058.
5	CUI J P , WU Y , SU X C , et al. A task allocation model for a team of aircraft launching on the carrier[J]. Mathematical Problems in Engineering, 2018, 2018, 7920806.
6	LIU J , HAN W , LI J , et al. Integration design of sortie scheduling for carrier aircrafts based on hybrid flexible flowshop[J]. IEEE System Journal, 2020, 14 (1): 1503- 1511. doi: 10.1109/JSYST.2019.2922261
7	苏析超, 李聪颖, 陈志刚. 混合差分进化算法在舰载机出动调度中的应用[J]. 计算机仿真, 2015, 32 (4): 74- 78.
31	MANTEGNA R N . Fast accurate algorithm for numerical simulation of Levy stable stochastic processes[J]. Physical Review E, 1994, 49 (5): 4677- 4683.
7	SU X C , LI C Y , CHEN Z G . Hybrid differential evolution algorithm for sortie scheduling of carrier aircraft[J]. Computer Simulation, 2015, 32 (4): 74- 78.
8	万兵, 韩维, 梁勇, 等. 舰载机出动离场调度优化算法[J]. 系统工程与电子技术, 2021, 43 (12): 3624- 3634.
	WAN B , HAN W , LIANG Y , et al. Optimization algorithm of carrier-based aircraft sortie departure scheduling[J]. Systems Engineering and Electronics, 2021, 43 (12): 3624- 3634.
9	KENNEDY J, EBERHART R. Particle swarm optimization[C]// Proc. of the International Conference on Neural Networks, 1995: 1942-1948
10	胡旺, GARYG Y, 张鑫. 基于Pareto熵的多目标粒子群优化算法[J]. 软件学报, 2014, 25 (5): 1025- 1050.
	HU W , GARY G Y , ZHANG X . Multiobjective particle swarm optimization based on Pareto entropy[J]. Journal of Software, 2014, 25 (5): 1025- 1050.
11	HARDHIENATA M K D, UGRINOVSKⅡ V, MERRICK K E. Task allocation under communication constraints using motivated particle swarm optimization[C]//Proc. of the IEEE Congress on Evolutionary Computation, 2014: 3135-3142.
12	XU L , WANG J , LI Y P , et al. Resource allocation algorithm based on hybrid particle swarm optimization for multiuser cognitive OFDM network[J]. Expert Systems with Applications, 2015, 42 (20): 7186- 7194. doi: 10.1016/j.eswa.2015.05.012
13	CHENG Z , WANG E G , TANG Y X , et al. Real-time path planning strategy for UAV based on improved particle swarm optimization[J]. Journal of Computers, 2014, 9 (1): 209- 214.
14	唐贤伦, 周维, 张衡, 等. 一种基于多目标混沌PSO的机器人足球防守策略[J]. 系统仿真学报, 2014, 26 (1): 51- 55.
	TANG X L , ZHOU W , ZHANG H , et al. Robot soccer defensive strategy based on multi-objective chaotic PSO[J]. Journal of System Simulation, 2014, 26 (1): 51- 55.
15	杨强大, 张卫军, 牛大鹏. 基于改进PSO的发酵过程同步串联混合建模[J]. 自动化学报, 2015, 41 (3): 620- 630.
	YANG Q D , ZHANG W J , NIU D P . Simultaneous series hybrid modeling for fermentation process based on improved particle swarm optimization[J]. Acta Automatica Sinica, 2015, 41 (3): 620- 630.
16	YUAN G L , ZHU L , YU T . Reactive power optimization based on parallel immune particle swarm optimization[J]. Journal of Computers, 2014, 9 (9): 2198- 2205.
17	MENG X L , WANG N J , LIU J , et al. Dynamic planning of aircraft sortie generation based on multiobjective optimization[J]. Scientific Programming, 2022, 2022, 6180618.
18	万兵, 韩维, 苏析超, 等. 基于CE-PF算法的舰载机离场调度优化问题研究[J]. 北京航空航天大学学报, 2022, 48 (5): 771- 785.
	WAN B , HAN W , SU X C , et al. Carrier-based aircraft departure scheduling optimization based on CE-PF algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48 (5): 771- 785.
19	刘洁, 董献洲, 韩维, 等. 采用牛顿迭代保辛伪谱算法的舰载机甲板路径规划[J]. 浙江大学学报(工学版), 2020, 54 (9): 1827- 1838.
	LIU J , DONG X Z , HAN W , et al. Trajectory planning for carrier aircraft on deck using Newton symplectic pseudo-spectral method[J]. Journal of Zhejiang University (Engineering Science), 2020, 54 (9): 1827- 1838.
20	毛琪波, 余震虹, 王相淳. 嵌入列维变异的混合动态粒子群算法[J]. 计算机工程与应用, 2017, 53 (8): 132-136, 239.
	MAO Q B , YU Z H , WANG X C . Hybrid PSO algorithm of dynamic topology with Levy mutation[J]. Computer Engineering and Applications, 2017, 53 (8): 132-136, 239.
21	闫群民, 马瑞卿, 马永翔, 等. 一种自适应模拟退火粒子群优化算法[J]. 西安电子科技大学学报, 2021, 48 (4): 120- 127.
	YAN Q M , MA R Q , MA Y X , et al. Adaptive simulated annealing particle swarm optimization algorithm[J]. Journal of Xidian University, 2021, 48 (4): 120- 127.
22	孙一凡, 张纪会. 基于模拟退火机制的自适应粘性粒子群算法[J]. 控制与决策, 2023, 38 (10): 2764- 2772.
	SUN Y F , ZHANG J H . Adaptive stickiness particle swarm optimization algorithm based on simulated annealing mechanism[J]. Control and Decision, 2023, 38 (10): 2764- 2772.
23	徐小琴, 王博, 赵红生, 等. 基于布谷鸟搜索和模拟退火算法的两电压等级配网重构方法[J]. 电力系统保护与控制, 2020, 48 (11): 84- 91.
	XU X Q , WANG B , ZHAO H S , et al. Reconfiguration of two-voltage distribution network based on cuckoo search and simulated annealing algorithm[J]. Power System Protection and Control, 2020, 48 (11): 84- 91.
24	鲁伟, 宋荣方. 基于模拟退火的多核多用户任务卸载调度[J]. 计算机技术与发展, 2021, 31 (6): 76- 80.
	LU W , SONG R F . Multi-core multi-user task offloading scheduling based on simulated annealing algorithm[J]. Computer Technology and Development, 2021, 31 (6): 76- 80.
25	杨玮, 李然, 张堃. 基于变邻域模拟退火算法的多自动导引车任务分配优化[J]. 计算机应用, 2021, 41 (10): 3056- 3062.
	YANG W , LI R , ZHANG K . Task allocation optimization for automated guided vehicles based on variable neighborhood simulated annealing algorithm[J]. Journal of Computer Applications, 2021, 41 (10): 3056- 3062.
26	陈科胜, 鲜思东, 郭鹏. 求解旅行商问题的自适应升温模拟退火算法[J]. 控制理论与应用, 2021, 38 (2): 245- 254.
	CHEN K S , XIAN S D , GUO P . Adaptive temperature rising simulated annealing algorithm for traveling salesman problem[J]. Control Theory and Applications, 2021, 38 (2): 245- 254.
27	费腾. 改进人工鱼群算法及其在物流选址优化中的应用研究[D]. 天津: 天津大学, 2016.
	FEI T. Improved artificial fish swarm algorithm and its application in logistics location optimization[D]. Tianjin: Tianjin University, 2016.
28	ISMAIL K . A comprehensive analysis of grid-based wind turbine layout using an efficient binary invasive weed optimization algorithm with Levy flight[J]. Expert Systems with Applications, 2022, 198, 116835. doi: 10.1016/j.eswa.2022.116835
29	BALAKRISHNAN K , DHANALAKSHMI R , UTKARSH M K . Improved Salp-Swarm algorithm based on the Levy flight for feature selection[J]. The Journal of Supercomputing, 2021, 77 (11): 12399- 12419. doi: 10.1007/s11227-021-03773-w
30	ZHANG Q T , FANG L Q , ZHAO Y L . Double subgroups fruit fly optimization algorithm with characteristics of Levy flight[J]. Journal of Computer Applications, 2015, 35 (5): 1348- 1352.

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