交互策略改进MOFA进化的多UAV协同航迹规划

doi:10.12305/j.issn.1001-506X.2021.08.30

摘要/Abstract

摘要：

针对无人机(unmanned aerial vehicle, UAV)多目标优化协同航迹规划方法中Pareto最优解集规模随迭代增长, 难以选择适合UAV任务特点的协同航迹等问题, 提出一种基于交互策略改进多目标萤火虫(multi-objective firefly algorithm, MOFA)进化的多UAV协同航迹规划方法。首先，采用变量分解策略将萤火虫算法中大规模变量分解成多个子种群, 以降低算法搜索的复杂度; 然后, 利用Tent混沌初始化和多种群循环分裂合并策略提高多目标萤火虫算法的搜索性能; 采用双极偏好占优机制、并设计协同度指标在Pareto最优解集中选取适合任务需要且协同度较高的UAV协同航迹。仿真实验表明, 所提方法能够根据任务设定生成对应侧重点、且满足协同性的相对最优航迹集, 证明了该方法的有效性。

关键词: UAV航迹规划, 集群协同, 多目标优化, 萤火虫算法, 种群多样性

Abstract:

In view of the problem that the number of Pareto optimal solution sets in the unmanned aerial vehicle (UAV) multi objective path planning method increases with iteration, it is difficult to choose the cooperative path suitable for the task, a multi-UAV cooperative path planning based on improved multi-objective firefly algorithm (MOFA) evolution of interactive strategy is proposed. First, the variable decomposition strategy is used to decompose large scale variables in the firefly algorithm into multiple subpopulations to reduce algorithm search complexity. Then, the Tent chaos initialization strategy and multiple population cycle split merge strategy are used to improve the search performance of the algorithm. Bipolar preference dominance is used and designed the cooperation index to select the cooperative path suitable for the task in the Pareto optimal solution set. The simulation experiments show that the proposed algorithm can accurately find the optimal route planning scenarios that satisfies the focus and synergy according to the task setting, and demonstrate that the efficiently of the proposed algorithm.

Key words: UAV path planning, swarm cooperation, multi objective optimization, firefly algorithm, population diversity

中图分类号:

V249

来磊, 邹鲲, 吴德伟, 李保中. 交互策略改进MOFA进化的多UAV协同航迹规划[J]. 系统工程与电子技术, 2021, 43(8): 2282-2289.

Lei LAI, Kun ZOU, Dewei WU, Baozhong LI. Multi-UAV cooperative path planning based on improved MOFA evolution of interactive strategy[J]. Systems Engineering and Electronics, 2021, 43(8): 2282-2289.

图/表 5

参考文献 31

1	张菁, 何友, 彭应宁, 等. 基于神经网络和人工势场的协同博弈路径规划[J]. 航空学报, 2019, 40 (3): 322493.
	ZHANG J , HE Y , PENG Y N , et al. Neural network and artificial potential field based cooperative and adversarially path planning[J]. Acta Aeronautica et Astronautica Sinica, 2019, 40 (3): 322493.
2	王晓芳, 柴劲, 周健. 基于分段贝塞尔曲线的多导弹协同航迹规划[J]. 系统工程与电子技术, 2018, 40 (10): 2317- 2324. doi: 10.3969/j.issn.1001-506X.2018.10.24
	WANG X F , CHAI J , ZHOU J . Cooperative path planning for multiple missiles based on piecewise Bezier curve[J]. Systems Engineering and Electronics, 2018, 40 (10): 2317- 2324. doi: 10.3969/j.issn.1001-506X.2018.10.24
3	高颖, 陈旭, 周士军. 基于改进蚁群算法的多批次协同三维航迹规划[J]. 西北工业大学学报, 2016, 34 (1): 41- 46. doi: 10.3969/j.issn.1000-2758.2016.01.006
	GAO Y , CHEN X , ZHOU S J . Planning based on improved ant colony algorithm multiple batches collaborative three dimensional track[J]. Journal of Northwestern Polytechnical University, 2016, 34 (1): 41- 46. doi: 10.3969/j.issn.1000-2758.2016.01.006
4	赵明, 赵玲玲, 苏小红. 一种三维多UAV协同航迹规划的空间模糊文化算法[J]. 哈尔滨工业大学学报, 2015, 47 (10): 29- 34. doi: 10.11918/j.issn.0367-6234.2015.10.007
	ZHAO M , ZHAO L L , SU X H . A cultural algorithm with spatial fuzzy set to solve multi UAVs cooperative path planning in a three dimensional environment[J]. Journal of Harbin Institute of Technology, 2015, 47 (10): 29- 34. doi: 10.11918/j.issn.0367-6234.2015.10.007
5	ZHANG D F , DUAN H B . Social class pigeon-inspired optimization and time stamp segmentation for multi-UAV cooperative path planning[J]. Neurocomputing, 2018, 313, 229- 246. doi: 10.1016/j.neucom.2018.06.032
6	JON V S , OLAF H G , JAN D B . Multi-UAV cooperative path planning for sensor placement using cooperative coevolving genetic strategy[J]. Lecture Notes in Computer Science, 2017, 10386 (1): 433- 444.
7	AYARI A , BOUAMAMA S . Dynamic distributed PSO joints elites in multiple robot path planning systems: theoretical and practical review of new ideas[J]. Procedia Computer Science, 2017, 112, 1082- 1091. doi: 10.1016/j.procs.2017.08.128
8	周德云, 王鹏飞, 李枭扬. 基于多目标优化算法的多无人机协同航迹规划[J]. 系统工程与电子技术, 2017, 39 (4): 782- 787.
	ZHOU D Y , WANG P F , LI X Y . Cooperative path planning of multi-UAV based on multi objective optimization algorithm[J]. Systems Engineering and Electronics, 2017, 39 (4): 782- 787.
9	庞强伟, 胡永江, 李文广. 多无人机多目标协同侦察航迹规划算法[J]. 中国惯性技术学报, 2019, 27 (3): 340- 348.
	PANG Q W , HU Y J , LI W G . Path planning algorithm for multi UAVs cooperative reconnaissance on multiple targets[J]. Journal of Chinese Inertial Technology, 2019, 27 (3): 340- 348.
10	MA Y , HU M Q , YAN X P . Multi-objective path planning for unmanned surface vehicle with currents effects[J]. ISA Transaction, 2018, 75, 137- 156. doi: 10.1016/j.isatra.2018.02.003
11	WANG B F , LI S , GUO J , et al. Car-like mobile robot path planning in rough terrain using multi-objective particle swarm optimization algorithm[J]. Neurocomputing, 2018, 282, 42- 51. doi: 10.1016/j.neucom.2017.12.015
12	MILAD N , ESMAEEL K , SAMIRA D . Multi-objective multi-robot path planning in continuous environment using an enhanced genetic algorithm[J]. Expert Systems with Applications, 2019, 115, 106- 120. doi: 10.1016/j.eswa.2018.08.008
13	ALEJANDRO H P , MIGUEL A , VEGA R . Solving the multi-objective path planning problem in mobile robotics with a firefly-based approach[J]. Soft Computing, 2017, 21 (4): 949- 964. doi: 10.1007/s00500-015-1825-z
14	LYV L , FAN T H , LI Q . Object tracking with improved firefly algorithm[J]. International Journal of Computing Science and Mathematics, 2018, 9 (3): 219- 231. doi: 10.1504/IJCSM.2018.093158
15	ANTONIO B , PATRICIA R R , FRANCISCO T R . A hyperbolastic type-I diffusion process: parameter estimation by means of the firefly algorithm[J]. Biosystems, 2018, 163, 11- 22. doi: 10.1016/j.biosystems.2017.11.001
16	LI P W , ZHAO J , XIE Z F , et al. General central firefly algorithm based on different learning time[J]. International Journal of Computing Science and Mathematics, 2017, 8 (5): 447- 456. doi: 10.1504/IJCSM.2017.088017
17	FEHMI B O , ADIL B . Quantum firefly swarms for multimodal dynamic optimization problems[J]. Expert Systems with Application, 2019, 115, 189- 199. doi: 10.1016/j.eswa.2018.08.007
18	ISHANI A , ANJU S . Software fault prediction using firefly algorithm[J]. International Journal of Intelligent Engineering Informatics, 2018, 6 (3-4): 356- 377.
19	YANG X S . Multiobjective firefly algorithm for continuous optimization[J]. Engineering with Computer, 2013, 29 (2): 175- 184. doi: 10.1007/s00366-012-0254-1
20	LYV L , ZHAO J , WANG J Y , et al. Multi-objective firefly algorithm based on compensation factor and elite learning[J]. Future Generation Computer System, 2019, 91, 37- 47. doi: 10.1016/j.future.2018.07.047
21	SERGIO S J , MIGUEL A , VEGA R . Improving multiobjective phylogenetic searches by using a parallel epsilon-dominance based adaptation of the firefly algorithm[J]. Lecture Notes in Computer Science, 2017, 10104, 384- 396.
22	MARCO B , ANITA S . Dominance for multi-objective robust optimization concepts[J]. European Journal of Operational Research, 2019, 273 (2): 430- 440. doi: 10.1016/j.ejor.2018.08.020
23	SUJARANI R , MANIVANNAN D . A nonlinear two dimensional logistic tent map for secure image communication[J]. International Journal of Information and Computer Security, 2018, 10 (2-3): 201- 215.
24	OMID T , AYAZ I , HOSSEIN J K . A new hybrid strategy for data clustering using cuckoo search based on Mantegna levy distribution, PSO and k-means[J]. International Journal of Computer Applications in Technology, 2018, 58 (2): 137- 149. doi: 10.1504/IJCAT.2018.094576
25	HU X P , XIU Y , CAO J . Recombined multinomial tree based on saddle point approximation and its application to Levy models options pricing[J]. Journal of Computational and Applied Mathematics, 2019, 346, 432- 439. doi: 10.1016/j.cam.2018.07.017
26	SAID B L , BECHIKH S , GHEDIRA K . The r-dominance: a new dominance relation for interactive evolutionary multicriteria decision making[J]. IEEE Trans.on Evolutionary Computation, 2010, 14 (5): 801- 818. doi: 10.1109/TEVC.2010.2041060
27	MARCO B , ANITA S . Dominance for multi objective robust optimization concepts[J]. European Journal of Operational Research, 2019, 273 (2): 430- 440. doi: 10.1016/j.ejor.2018.08.020
28	ZHANG H L , WANG C , FAN W H . A new filter collaborative state transition algorithm for two objective dynamic reactive power optimization[J]. Tsinghua Science and Technology, 2019, 24 (1): 30- 43. doi: 10.26599/TST.2018.9010068
29	MAURICIO B L P R V P . A evolutionary algorithm for the multi objective pick up and delivery pollution routing problem[J]. International Transactions in Operational Research, 2019, 26 (1): 302- 317. doi: 10.1111/itor.12376
30	邱飞岳, 吴裕市, 邱启仓, 等. 基于双极偏好占优的高维目标进化算法[J]. 软件学报, 2013, 24 (3): 476- 489.
	QIU F Y , WU Y S , QIU Q C , et al. Many-objective evolutionary algorithm based on bipolar preferences dominance[J]. Journal of Software, 2013, 24 (3): 476- 489.
31	黄长强, 赵克新. 基于改进蚁狮算法的无人机三维航迹规划[J]. 电子与信息学报, 2018, 40 (7): 1532- 1537.
	HUANG C Q , ZHAO K X . Three dimensional path planning of UAV with improved ant lion optimizer[J]. Journal of Electronics Information Technology, 2018, 40 (7): 1532- 1537.

[1]	闫世瑛, 颜克斐, 方伟, 陆恒杨. 基于差分进化邻域自适应的大规模多目标算法[J]. 系统工程与电子技术, 2022, 44(7): 2112-2124.
[2]	刘乾, 鲁云军, 陈克斌, 韩梦瑶, 郭亮. 任务主体二元约束下作战任务分解EVA方法[J]. 系统工程与电子技术, 2022, 44(7): 2201-2210.
[3]	张大力, 夏红伟, 张朝兴, 马广程, 王常虹. 改进萤火虫算法及其收敛性分析[J]. 系统工程与电子技术, 2022, 44(4): 1291-1300.
[4]	尹洋, 杨全顺, 王征, 刘洋. 通信距离约束下的无人船集群覆盖搜索方法[J]. 系统工程与电子技术, 2022, 44(12): 3821-3828.
[5]	刘钢, 汤俊, 刘陈, 李武. 无人飞行器集群协同行为建模技术综述[J]. 系统工程与电子技术, 2021, 43(8): 2221-2231.
[6]	崔荣伟, 韩维, 苏析超, 王立国, 刘玉杰. 舰载机甲板机务勤务保障作业调度与资源配置集成优化[J]. 系统工程与电子技术, 2021, 43(7): 1884-1893.
[7]	周晶, 赵晓哲, 许震, 林众, 张晓盼. 基于D-NSGA-Ⅲ算法的无人机群高维多目标任务分配方法[J]. 系统工程与电子技术, 2021, 43(5): 1240-1247.
[8]	夏博远, 杨克巍, 杨志伟, 张小可, 赵丹玲. 基于杀伤网评估的装备组合多目标优化[J]. 系统工程与电子技术, 2021, 43(2): 399-409.
[9]	田春明, 杨安, 叶乐, 李建星, 贺雨晨. 基于贝叶斯算法的天线端到端优化[J]. 系统工程与电子技术, 2021, 43(12): 3413-3419.
[10]	来磊, 吴德伟, 邹鲲, 韩昆, 李海林. 基于多准则交互膜进化算法的UAV三维航迹规划[J]. 系统工程与电子技术, 2021, 43(1): 138-146.
[11]	王亚东, 石全, 夏伟, 陈材. 基于超启发式算法的备件供应网络结构优化[J]. 系统工程与电子技术, 2020, 42(3): 620-629.
[12]	李岩, 陈云翔, 罗承昆, 蔡忠义. 基于FA-ER的装备保障资源供给能力评估方法[J]. 系统工程与电子技术, 2020, 42(3): 630-637.
[13]	丁春山. 传感器管理技术研究现状与展望[J]. 系统工程与电子技术, 2020, 42(12): 2761-2770.
[14]	马武彬, 王锐, 王威超, 吴亚辉, 邓苏, 黄宏斌. 基于进化多目标优化的微服务组合部署与调度策略[J]. 系统工程与电子技术, 2020, 42(1): 90-100.
[15]	李瑞阳, 王智学, 禹明刚, 何红悦. 基于鲁棒能力的体系多目标组合优化[J]. 系统工程与电子技术, 2019, 41(5): 1034-1042.