Application of Improved Simulated Annealing Genetic Algorithm in Task Assignment of Swarm of Drones

Huang, Kai; Dong, Yu Cai; Wang, DongNing; Wang, Shuo

doi:10.1109/ispds51347.2020.00062

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…In paper [21], for the task allocation problem of the urban road traffic information collection sensor network for the collaborative collection of complex traffic parameters, the sensor network is mapped to a multiagent system, and the task completion time, node energy consumption, and network load balance are used as the evaluation function. The authors used alliance-based collaborative methods to construct a nonlinear multiobjective optimization model of sensor network task allocation.…”

Section: Related Workmentioning

confidence: 99%

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An Improved Adaptive Clone Genetic Algorithm for Task Allocation Optimization in ITWSNs

Zha

Xiao

et al. 2021

Journal of Sensors

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Research on intelligent transportation wireless sensor networks (ITWSNs) plays a very important role in an intelligent transportation system. ITWSNs deploy high-yield and low-energy-consumption traffic remote sensing sensor nodes with complex traffic parameter coordination on both sides of the road and use the self-organizing capabilities of each node to automatically establish the entire network. In the large-scale self-organization process, the importance of tasks undertaken by each node is different. It is not difficult to prove that the task allocation of traffic remote sensing sensors is an NP-hard problem, and an efficient task allocation strategy is necessary for the ITWSNs. This paper proposes an improved adaptive clone genetic algorithm (IACGA) to solve the problem of task allocation in ITWSNs. The algorithm uses a clonal expansion operator to speed up the convergence rate and uses an adaptive operator to improve the global search capability. To verify the performance of the IACGA for task allocation optimization in ITWSNs, the algorithm is compared with the elite genetic algorithm (EGA), the simulated annealing (SA), and the shuffled frog leaping algorithm (SFLA). The simulation results show that the execution performance of the IACGA is higher than EGA, SA, and SFLA. Moreover, the convergence speed of the IACGA is faster. In addition, the revenue of ITWSNs using IACGA is higher than those of EGA, SA, and SFLA. Therefore, the proposed algorithm can effectively improve the revenue of the entire ITWSN system.

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Section: Related Workmentioning

confidence: 99%

“…In order to better understand the crossover process, we use formulas (20) and (21) to visualize it. We chose the crossover point at the 5 th gene sequence and swapped all sequences from then on:…”

Section: Selectionmentioning

confidence: 99%

An Improved Adaptive Clone Genetic Algorithm for Task Allocation Optimization in ITWSNs

Zha

Xiao

et al. 2021

Journal of Sensors

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“…At the same time, CSA can be used to solve multiobjective problems. Comparing CSA with the GA [19][20][21], the main difference is the way the population evolves. In the GA, the population evolves through crossover and mutation, and in the CSA, cell reproduction is asexual, with each offspring produced by one cell being an exact copy of its parents, and mutation and selection are made through these offspring.…”

Section: Introductionmentioning

confidence: 99%

UAV Task Allocation Based on Clone Selection Algorithm

Zhang

Chen²

2021

Wireless Communications and Mobile Computing

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With the continuous development of computer and network technology, the large-scale and clustered operations of drones have gradually become a reality. How to realize the reasonable allocation of UAV cluster combat tasks and realize the intelligent optimization control of UAV cluster is one of the most challenging difficulties in UAV cluster combat. Solving the task allocation problem and finding the optimal solution have been proven to be an NP-hard problem. This paper proposes a CSA-based approach to simultaneously optimize four objectives in multi-UAV task allocation, i.e., maximizing the number of successfully allocated tasks, maximizing the benefits of executing tasks, minimizing resource costs, and minimizing time costs. Experimental results show that, compared with the genetic algorithm, the proposed method has better performance on solving the UAV task allocation problem with multiple objectives.

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“…[22] also uses a clustering algorithm, but in this case, it is k-means instead of CSCM. In [23], SA is used inside a GA to prevent the GA from stalling in local minima and, as a result, improves the compilation time with respect to a normal GA for the same task. Ref.…”

mentioning

confidence: 99%

Application of Deep Reinforcement Learning to UAV Swarming for Ground Surveillance

Arranz,

Carramiñana,

Miguel

et al. 2023

Sensors

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This paper summarizes in depth the state of the art of aerial swarms, covering both classical and new reinforcement-learning-based approaches for their management. Then, it proposes a hybrid AI system, integrating deep reinforcement learning in a multi-agent centralized swarm architecture. The proposed system is tailored to perform surveillance of a specific area, searching and tracking ground targets, for security and law enforcement applications. The swarm is governed by a central swarm controller responsible for distributing different search and tracking tasks among the cooperating UAVs. Each UAV agent is then controlled by a collection of cooperative sub-agents, whose behaviors have been trained using different deep reinforcement learning models, tailored for the different task types proposed by the swarm controller. More specifically, proximal policy optimization (PPO) algorithms were used to train the agents’ behavior. In addition, several metrics to assess the performance of the swarm in this application were defined. The results obtained through simulation show that our system searches the operation area effectively, acquires the targets in a reasonable time, and is capable of tracking them continuously and consistently.

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Application of Improved Simulated Annealing Genetic Algorithm in Task Assignment of Swarm of Drones

Cited by 6 publications

References 25 publications

An Improved Adaptive Clone Genetic Algorithm for Task Allocation Optimization in ITWSNs

An Improved Adaptive Clone Genetic Algorithm for Task Allocation Optimization in ITWSNs

UAV Task Allocation Based on Clone Selection Algorithm

Application of Deep Reinforcement Learning to UAV Swarming for Ground Surveillance

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