Research on task allocation of UAV cluster based on particle swarm quantization algorithm

Geng, Rongmei; Ji, Renxin; Zi, Shuanjin

doi:10.3934/mbe.2023002

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…In addition, dynamic problems in tasking problems are common. Additionally, the PSO algorithm has been employed in additional combinations as a heuristic method: in their studies, Geng et al [26] proposed a quantified particle swarm optimization algorithm for the task allocation problem of UAV clusters;. Shao et al [27] proposed a hybrid strategy based on discrete particle swarm optimization for the many-to-one task planning problem in the case of a constructed a quantized particle swarm optimization technique.…”

Section: Algorithm For Solving Uav Task Assignmentmentioning

confidence: 99%

A Drone Scheduling Method for Emergency Power Material Transportation Based on Deep Reinforcement Learning Optimized PSO Algorithm

Zai,

Wang,

2023

Sustainability

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Stable material transportation is essential for quickly restoring the power system following a disaster. Drone-based material transportation can bypass ground transportation’s limitations and reduce transit times. However, the current drone flight trajectory distribution optimization model cannot meet the need for mountainous emergency relief material distribution following a disaster. A power emergency material distribution model with priority conditions is proposed in this paper, along with a two-layer dynamic task-solving framework that takes task dynamics into account. This research proposes an algorithm (TD3PSO) that combines the particle swarm algorithm (PSO) updating technique with the double-delay depth deterministic policy gradient algorithm (TD3) algorithm’s capacity to dynamically parameterize. The final task allocation experiment demonstrates that the modified TD3PSO significantly outperforms the conventional algorithm on the Solomon data set, with an improvement of 26.3% on average over the RLPSO algorithm and a 11.0% reduction in the volatility of the solving impact. When solving under realistic circumstances, the solution effect increases by 1.6% to 13.4%, and the redistribution experiment confirms the framework’s efficacy. As a result, the algorithm and architecture suggested in this paper may successfully address the issue of scheduling drones for power emergencies while enhancing transportation efficiency.

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Section: Algorithm For Solving Uav Task Assignmentmentioning

confidence: 99%

A Drone Scheduling Method for Emergency Power Material Transportation Based on Deep Reinforcement Learning Optimized PSO Algorithm

Zai,

Wang,

2023

Sustainability

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“…Due to the advantages of heuristic search methods such as genetic algorithm [18][19][20][21], ant colony algorithm [18,[22][23][24], simulated annealing algorithm [3,25,26], and particle swarm optimization algorithm [27,28] in solving nonlinear problems, they have become mainstream tools in current research on train energy-efcient optimization. Various heuristic search methods are combined into different energy-efcient models to obtain the optimal combination of driving regimes.…”

Section: Introductionmentioning

confidence: 99%

Driving Strategy Using an Improved Ant Colony System for Energy-Efficient Train

Yang,

Miao,

Wang

2024

Journal of Advanced Transportation

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Optimal energy-efficient train operation optimization is one of the widely studied areas in transportation science, which can significantly reduce energy consumption that accounts for a large proportion of operating costs. In order to adapt to the complex and changeable railway line conditions such as gradient, slope length, and speed limit and avoid the error in tracking speed curve, an optimal driving strategy decision-making (ODSD) model is proposed in this paper. The model considers the non-fixed sequence of driving regimes, and the regimes are directly selected in the discrete micro-subsegments of an equal time-division pattern. To solve this model efficiently, an improved ant colony system algorithm with the difference edges (ACSd) is proposed, which takes the heuristic effect of the difference between the best solutions of two adjacent iterations, i.e., “the difference edges,” into account. Additionally, energy-efficient heuristic factor and speed heuristic factor are presented to balance energy saving and speed. The results demonstrate that ACSd performs better than the basic ant colony system algorithm in solving traveling salesman problem (TSP) and provides more flexible driving strategies for the ODSD model.

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Dual-loop control and state prediction analysis of QUAV trajectory tracking based on biological swarm intelligent optimization algorithm

Zou,

Yang,

Zhao

2024

Sci Rep

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Quadrotor unmanned aerial vehicles (QUAVs) have attracted significant research focus due to their outstanding Vertical Take-Off and Landing (VTOL) capabilities. This research addresses the challenge of maintaining precise trajectory tracking in QUAV systems when faced with external disturbances by introducing a robust, two-tier control system based on sliding mode technology. For position control, this approach utilizes a virtual sliding mode control signal to enhance tracking precision and includes adaptive mechanisms to adjust for changes in mass and external disruptions. In controlling the attitude subsystem, the method employs a sliding mode control framework that secures system stability and compliance with intermediate commands, eliminating the reliance on precise models of the inertia matrix. Furthermore, this study incorporates a deep learning approach that combines Particle Swarm Optimization (PSO) with the Long Short-Term Memory (LSTM) network to foresee and mitigate trajectory tracking errors, thereby significantly enhancing the reliability and safety of mission operations. The robustness and effectiveness of this innovative control strategy are validated through comprehensive numerical simulations.

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Research on task allocation of UAV cluster based on particle swarm quantization algorithm

Cited by 5 publications

References 18 publications

A Drone Scheduling Method for Emergency Power Material Transportation Based on Deep Reinforcement Learning Optimized PSO Algorithm

A Drone Scheduling Method for Emergency Power Material Transportation Based on Deep Reinforcement Learning Optimized PSO Algorithm

Driving Strategy Using an Improved Ant Colony System for Energy-Efficient Train

Dual-loop control and state prediction analysis of QUAV trajectory tracking based on biological swarm intelligent optimization algorithm

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