[2]
N. Basilico and S. Carpin, Deploying teams of heterogeneous UAVs in cooperative two-level surveillance missions, in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), Hamburg, Germany, 2015, pp. 610–615.
[5]
T. M. Cabreira, P. R. Ferreira, C. Di Franco, and G. C. Buttazzo, Grid-based coverage path planning with minimum energy over irregular-shaped areas with uavs, in Proc. Int. Conf. Unmanned Aircraft Systems (ICUAS), Atlanta, GA, USA, 2019, pp. 758–767.
[7]
L. C. Batista da Silva, R. M. Bernardo, H. A. de Oliveira, and P. F. F. Rosa, Multi-UAV agent-based coordination for persistent surveillance with dynamic priorities, in Proc. Int. Conf. Military Technologies (ICMT), Brno, Czech Republic, 2017, pp. 765–771.
[15]
A. Whitbrook, Q. Meng, and P. W. H. Chung, A novel distributed scheduling algorithm for time-critical multi-agent systems, in Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), Hamburg, Germany, 2015, pp. 6451–6458.
[16]
J. Turner, Q. Meng, and G. Schaefer, Increasing allocated tasks with a time minimization algorithm for a search and rescue scenario, in Proc. IEEE Int. Conf. Robotics and Automation (ICRA), Seattle, WA, USA, 2015, pp. 3401–3407.
[26]
A. Tsourdos, B. White, and M. Shanmugavel, Cooperative Path Planning of Unmanned Aerial Vehicles, Chichester, UK: Wiley, 2010.
[29]
T. Shima and S. Rasmussen, UAV Cooperative Decision and Control: Challenges and Practical Approaches, Philadelphia, PA, USA: SIAM, 2008.
[30]
T. Shima and C. Schumacher, Assignment of cooperating UAVs to simultaneous tasks using genetic algorithms, in Proc. AIAA Guidance, Navigation, and Control Conf. and Exhibit, San Francisco, CA, USA, 2005, pp. 1–14.
[41]
J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, Cambridge, MA, USA: MIT Press, 1992.
[42]
K. A. De Jong, An analysis of the behavior of a class of genetic adaptive systems, Tech. Rep. University of Michigan, Ann Arbor, MI, USA, 1975.
[43]
M. Mitchell, An Introduction to Genetic Algorithms, Cambridge, MA, USA: MIT Press 1998.
[44]
D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Boston, MA, USA: Addison-Wesley Professional, 1989.
[47]
J. Kennedy and R. Eberhart, Particle swarm optimization, in Proc. Int. Conf. Neural Networks (ICNN’95), Perth, Australia, 1995, pp. 1942–1948.
[48]
J. Zhang, P. Wen, and A. Xiong, Application of improved quantum particle swarm optimization algorithm to multi-task assignment for heterogeneous UAVs, in Proc. 6th Asian Conf. Artificial Intelligence Technology (ACAIT), Changzhou, China, 2022, pp. 1–5.
[50]
A. Colorni, M. Dorigo, and V. Maniezzo, Distributed optimization by ant colonies, in Proc. 1st European Conf. Artificial Life (ECAL’91), Paris, France, 1991, pp. 134–142.
[51]
T. Stutzle and H. Hoos, MAX-MIN Ant System and local search for the traveling salesman problem, in Proc. 1997 IEEE Int. Conf. Evolutionary Computation (ICEC ’97), Indianapolis, IN, USA, 1997, pp. 309–314.
[54]
V. M. M. O. Ompusunggu, M. K. D. Hardhienata, and K. Priandana, Application of ant colony optimization for the selection of multi-UAV coalition in agriculture, in Proc. Int. Conf. Computer Science and Its Application in Agriculture (ICOSICA), Bogor, Indonesia, 2020, pp. 1–8.
[55]
Z. Wang, W. Zhang, and G. Li, UAV’s task allocation using multiple colonies of ants, in Proc. 2009 IEEE Int. Conf. Automation and Logistics, Shenyang, China, 2009, pp. 371–374.
[65]
V. Oriol, F. Meire, and J. Navdeep, Pointer networks, in Proc. 28th Int. Conf. Neural Information Processing Systems (NIPS’15), Montreal, Canada, 2015, pp. 2692–2700.
[66]
I. Bello, H. Pham, Q. V. Le, M. Norouzi, and S. Bengio, Neural combinatorial optimization with reinforcement learning, arXiv preprint arXiv: 1611.09940, 2016.
[67]
W Kool, H Van Hoof, and M Welling, Attention, learn to solve routing problems! arXiv preprint arXiv: 1803.08475, 2018.
[72]
Z. Wang, Q. Liu, H. Tao, and J. Li, Multiple task planning based on TS algorithm for multiple heterogeneous unmanned aerial vehicles, in Proc. 2014 IEEE Chinese Guidance, Navigation and Control Conf., Yantai, China, 2014, pp. 630–635.
[76]
H. Chen, J. Xu, and C. Wu, Multi-UAV task assignment based on improved Wolf Pack Algorithm, in Proc. 2020 Int. Conf. Cyberspace Innovation of Advanced Technologies, Guangzhou China, 2020, pp. 109–115.
[79]
J. Zhang, P. Wen, and A. Xiong, Multi-task Assignment Research for Heterogeneous UAVs based on Improved Simulated Annealing Particle Swarm Optimization Algorithm, in Proc. Int. Conf. Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), Suzhou, China, 2022, pp. 284–288.