无人机集群任务分配的多目标算法研究

高卫峰; 王琼; 李宏; 谢晋; 公茂果

doi:10.19665/j.issn1001-2400.20230413

您当前的位置：

首页 >

文章列表页 >

无人机集群任务分配的多目标算法研究

信息与通信工程 | 更新时间：2024-06-03

- 无人机集群任务分配的多目标算法研究
- Research on the multi-objective algorithm of UAV cluster task allocation
- 西安电子科技大学学报 2024年51卷第2期页码：1-12
- 作者机构：
  
  1. 西安电子科技大学数学与统计学院,陕西西安 710071
  2. 西安电子科技大学协同智能系统教育部重点实验室,陕西西安 710071
- 作者简介：
  
  [ "高卫峰(1985—),男,教授,E-mail:[email protected]; " ]
  [ "王琼(1999—),男,西安电子科技大学硕士研究生,E-mail:[email protected]; " ]
  [ "李宏(1972—),男,副教授,E-mail:[email protected]; " ]
  [ "谢晋(1990—),男,讲师,E-mail:[email protected]; " ]
  [ "公茂果(1979—),男,教授,E-mail:[email protected]" ]
- 基金信息：
  
  国家自然科学基金(62276202);国家自然科学基金(62106186)
- DOI：10.19665/j.issn1001-2400.20230413
  中图分类号： TP301.6
- 纸质出版日期：2024-4-20，
  
  网络出版日期：2023-9-15，
  
  收稿日期：2023-2-27，
扫描看全文
高卫峰, 王琼, 李宏, 等. 无人机集群任务分配的多目标算法研究[J]. 西安电子科技大学学报, 2024,51(2):1-12.

Weifeng GAO, Qiong WANG, Hong LI, et al. Research on the multi-objective algorithm of UAV cluster task allocation[J]. Journal of Xidian University, 2024,51(2):1-12.
高卫峰, 王琼, 李宏, 等. 无人机集群任务分配的多目标算法研究[J]. 西安电子科技大学学报, 2024,51(2):1-12. DOI： 10.19665/j.issn1001-2400.20230413.

Weifeng GAO, Qiong WANG, Hong LI, et al. Research on the multi-objective algorithm of UAV cluster task allocation[J]. Journal of Xidian University, 2024,51(2):1-12. DOI： 10.19665/j.issn1001-2400.20230413.

摘要

针对目标识别场景下的无人机集群协同任务分配问题

建立了以识别代价和识别收益为目标的优化模型

设计了基于分解的多目标差分进化算法求解该模型。首先

提出了精英初始化方法

在保证所得非支配解分布均匀的基础上

筛选初始解以提高解集的质量;其次

结合模型特性构造整数编码下的多目标差分进化算子

提高算法的收敛速度;最后

设计了带限制的禁忌搜索策略

使得算法具有跳出局部最优的能力。该算法为问题的求解提供一组非支配解集

使得可以根据实际需求选择更加合理的最优解。通过上述方法获得分配方案后

基于拍卖算法设计任务重分配策略

进一步调整分配方案

以应对无人机损毁的突发情况。仿真实验验证了所提算法在求解小、中、大规模任务分配问题上的有效性。相比于其他算法

文中算法所得非支配集具有更高的质量

可以消耗更少的识别代价来获取更高的识别收益

说明算法具有一定的优越性。

Abstract

Aiming at the cooperative task allocation problem of UAV swarm in target recognition scenario

an optimization model with recognition cost and recognition benefit as the goal is established

and a multi-objective differential evolution algorithm based on decomposition is designed to solve the model.First

an elite initialization method is proposed

and the initial solution is screened to improve the quality of the solution set on the basis of ensuring the uniform distribution of the obtained nondominated solution.Second

the multi-objective differential evolution operator under integer encoding is constructed based on the model characteristics to improve the convergence speed of the algorithm.Finally

a tabul search strategy with restrictions is designed

so that the algorithm has the ability to jump out of the local optimal.The algorithm provides a set of nondominated solution sets for the solution of the problem

so that a more reasonable optimal solution can be selected according to actual needs.After obtaining the allocation scheme by the above method

the task reallocation strategy is designed based on the auction algorithm

and the allocation scheme is further adjusted to cope with the unexpected situation of UAV damage.On the one hand

simulation experiments verify the effectiveness of the proposed algorithm in solving small

medium and large-scale task allocation problems

and on the other hand

compared with other algorithms

the nondominated set obtained by the proposed algorithm has a higher quality

which can consume less recognition cost and obtain higher recognition revenue

indicating that the proposed algorithm has certain advantages.

关键词

任务分配无人机多目标算法进化算法禁忌搜索

Keywords

task allocationunmanned aerial vehiclesmulti-objective optimizationevolutionary algorithmstabu search

references

LIN W C, LIAO D Y, LIU C Y, et al. Daily Imaging Scheduling of an Earth Observation Satellite[J]. IEEE Transactions on Systems,Man,and Cybernetics-Part A:Systems and Humans, 2005, 35(2):213-223.

GUO W Z, LI J, CHEN G L, et al. A PSO-Optimized Real-Time Fault-Tolerant Task Allocation Algorithm in Wireless Sensor Networks[J]. IEEE Transactions on Parallel and Distributed Systems, 2015, 26(12):3236-3249.

TAL S, STEVEN J R, ANDREW G S, et al. Multiple Task Assignments for Cooperating Uninhabited Aerial Vehicles Using Genetic Algorithms[J]. Computer and Operations Research, 2006, 33(11):3252-3269.

黄刚, 李军华. 基于AC-DSDE进化算法多UAVs协同目标分配[J]. 自动化学报, 2021, 47(1):173-184.

HUANG Gang, LI Junhua. Multi-UAV Cooperative Target Allocation Based on AC-DSDE Evolutionary Algorithm[J]. Acta Automatica Sinica, 2021, 47(1):173-184.

JANA B, CHAKRABORTY M, MANDAL T. A Task Scheduling Technique Based on Particle Swarm Optimization Algorithm in Cloud Environment[J]. Advances in Intelligent Systems and Computing, 2019,742:525-536.

ALENCAR R C, SANTANA C J, BASTOS-FILHO C J A. Optimizing Routes for Medicine Distribution Using Team Ant Colony System[J]. Advances in Intelligent Systems and Computing, 2020,923:40-49.

LUO J P, LIU Q Q, YANG Y, et al. An Artificial Bee Colony Algorithm for Multi-Objective Optimization[J]. Applied Soft Computing, 2017,50:235-251.

DEB K, PRATAP A, AGARWAL S, et al. A Fast and ElitistMultiobjective Genetic Algorithm:NSGA-Ⅱ[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2):182-197.

ZHANG Q F, LI H. MOEA/D:A Multi-Objective Evolutionary Algorithm Based on Decomposition[J]. IEEE Transactions on Evolutionary Computation, 2007, 11(6):712-731.

刘天宇, 曹磊. 多任务机制驱动的高维多目标进化算法[J]. 西安电子科技大学学报, 2022, 49(4):134-143.

LIU Tianyu, CAO Lei. Many-Objective Evolutionary Algorithm Based on the Multitasking Mechanism[J]. Journal of Xidian University, 2022, 49(4):134-143.

ZHANG C J, TAN K C, LEE L H, et al. Adjust Weight Vectors in MOEA/D for Bi-Objective Optimization Problems with Discontinuous Pareto Fronts[J]. Soft Computing:A Fusion of Foundations, Methodologies and Application, 2018, 22(12):3997-4012.

HUA Y, JIN Y, HAO K. A Clustering-Based Adaptive Evolutionary Algorithm for Multiobjective Optimization with Irregular Pareto Fronts[J]. IEEE Transactions on Cybernetics, 2019, 49(7):2758-2770. DOI:10.1109/TCYB.2018.2834466http://doi.org/10.1109/TCYB.2018.2834466

STORN R, PRICE K. Differential Evolution-A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces[J]. Journal of Global Optimization, 1997,11:341-359.

LARA A, SANCHEZ G, COELLO C A C, et al. HCS:A New Local Search Strategy for Memetic Multiobjective Evolutionary Algorithms[J]. IEEE Transactions on Evolutionary Computation, 2010, 14(1):112-132.

CHENG R, JIN Y C, OLHOFER M, et al. A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization[J]. IEEE Transaction on Evolutionary Computation, 2016, 20(5):773-791.

LIANG Z P, LUO T T, HU K F, et al. An Indicator-Based Many-Objective Evolutionary Algorithm with Boundary Protection[J]. IEEE Transactions on Cybernetics, 2021, 51(9):4553-4566.

封文清, 巩敦文. 基于在线感知Pareto前沿划分目标空间的多目标进化优化[J]. 自动化学报, 2020, 46(8):1628-1643.

FENG Wenqing, GONG Dunwen. Multi-Objective Evolutionary Optimization with Objective Space Partition Based on Online Perception of Pareto Front[J]. Acta Automatica Sinica, 2020, 46(8):1628-1643.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

面向韧性拓扑构造的无人机集群功率分配策略

灾后无人机自组网高动态多信道TDMA调度算法

灾后无人机不确定偏好序下稳定中继选择方法

多任务机制驱动的高维多目标进化算法

基于柯西变异的免疫单克隆策略