This paper presents an unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) data collection system, where a UAV collects data from IoT devices at various stop points and returns to its starting point. Our g...
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This paper presents an unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) data collection system, where a UAV collects data from IoT devices at various stop points and returns to its starting point. Our goal is to minimize energy consumption by jointly optimizing the UAV's deployment and flight trajectory. This problem is complex and NP-hard. To address this, this paper proposes the Differential Evolution with Variable Population Size and Route (DEVIPSR) algorithm, a single-level model that improves upon traditional Differential Evolution (DE). Each individual in the population represents both the position and order of stop points in the UAV's trajectory, allowing comprehensive optimization of deployment and flight planning. This paper also introduces a double replacement (DR) strategy and an initialization strategy to enhance convergence speed. The lkh algorithm is used to finalize the trajectory optimization. Experimental results show that DEVIPSR algorithm outperforms multi-level optimization models by reducing total energy consumption by approximately 18.26%.
作者：
Tarkov, M. S.Russian Acad Sci
Rzhanov Inst Semicond Phys Siberian Branch Pr Akad Lavrenteva 13 Novosibirsk 630090 Russia
A new algorithm(NWTA algorithm) for solving the traveling salesman problem (TSP) is proposed. The algorithm is based on the use of the Hopfield recurrent neural network, the "Winner takes all" (WTA) method f...
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A new algorithm(NWTA algorithm) for solving the traveling salesman problem (TSP) is proposed. The algorithm is based on the use of the Hopfield recurrent neural network, the "Winner takes all" (WTA) method for the cycle formation, and the 2-opt optimization method. A special feature of the algorithm proposed is in the use of the method of partial (prefix) sums to accelerate the solution of the system of the Hopfield network equations. For attaining additional acceleration, the parallelization of the algorithm proposed is performed on GPU with the CUDA technology. Several examples from the TSPLIB library with the number of cities from 51 to 2,392 show that the algorithm finds approximate solutions of the TSP (a relative increase in the length of the route with respect to the optimum is 0.03 divided by 0.14). With a large number of cities (130 and more), the NWTA algorithm running duration on the CPU is 4 divided by 24 times shorter than the duration of the heuristic lkh algorithm giving optimal solutions for all TSPLIB examples.
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