Recently, drone shows have impressed many people through a convergence of technology and art. However, these demonstrations have limited operating hours based on the battery life. Thus, it is important to minimize the unnecessary transition time between scenes without collision to increase operating time. This paper proposes a fast and energy-efficient scene transition algorithm that minimizes the transition times between scenes. This algorithm reduces the maximum drone movement distance to increase the operating time and exploits a multilayer method to avoid collisions between drones. In addition, a swarming flight system including robust communication and position estimation is presented as a concrete experimental system. The proposed algorithm was verified using the swarming flight system at a drone show performed with 100 drones.
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http://dx.doi.org/10.3390/s21041260 | DOI Listing |
Sci Rep
January 2025
School of Statistics and Applied Mathematics, Anhui University of Finance and Economics, Anhui, 10378, China.
Dung Beetle algorithm is an intelligent optimization algorithm with advantages in exploitation ability. However, due to the high randomness of parameters, premature convergence and other reasons, there is an imbalance between exploration and exploitation ability, and it is easy to fall into the problem of local optimal solution. The purpose of this study is to improve the optimization performance of dung beetle algorithm and explore its engineering application value.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Respiratory and Critical Care Medicine, The First Medical Centre of Chinese PLA General Hospital, Haidian District, Beijing, China.
Moth Flame Optimization (MFO) is a swarm intelligence algorithm inspired by the nocturnal flight mode of moths, and it has been widely used in various fields due to its simple structure and high optimization efficiency. Nonetheless, a notable limitation is its susceptibility to local optimality because of the absence of a well-balanced exploitation and exploration phase. Hence, this paper introduces a novel enhanced MFO algorithm (BWEMFO) designed to improve algorithmic performance.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Computer Science, College of Computer and Information Sciences, King Saud University, 11543, Riyadh, Saudi Arabia.
The traditional optimization approaches suffer from certain problems like getting stuck in local optima, low speed, susceptibility to local optima, and searching unknown search spaces, thus requiring reliance on single-based solutions. Herein, an Improved Aquila Optimizer (IAO) is proposed, which is a unique meta-heuristic optimization method motivated by the hunting behavior of Aquila. An improved version of Aquila optimizer seeks to increase effectiveness and productivity.
View Article and Find Full Text PDFBiomimetics (Basel)
December 2024
School of Electrical and Photoelectronic Engineering, West Anhui University, Lu'an 237012, China.
This work presents a rat optimization algorithm (ROA), which simulates the social behavior of rats and is a new nature-inspired optimization technique. The ROA consists of three operators that simulate rats searching for prey, chasing and fighting prey, and jumping and hunting prey to deal with optimization issues. The Levy flight strategy is introduced into the ROA to keep the algorithm from running into issues with slow convergence and local optimums.
View Article and Find Full Text PDFSensors (Basel)
November 2024
National Key Laboratory of Transient Physics, Nanjing University of Science and Technology, Nanjing 210094, China.
Path planning technology is of great consequence in the field of unmanned aerial vehicles (UAVs). In order to enhance the safety, path smoothness, and shortest path acquisition of UAVs undertaking tasks in complex urban multi-obstacle environments, this paper proposes an innovative composite improvement algorithm that integrates the advantages of the jellyfish search algorithm and the particle swarm algorithm. The algorithm effectively overcomes the shortcomings of a single algorithm, including parameter setting issues, slow convergence rates, and a tendency to become trapped in local optima.
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