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Search Results (4)

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Keywords = artificial bee colony-based COPE

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17 pages, 964 KiB  
Article
Reconfiguration for UAV Formation: A Novel Method Based on Modified Artificial Bee Colony Algorithm
by Zipeng Yang, Futing Yang, Tianqi Mao, Zhenyu Xiao, Zhu Han and Xianggen Xia
Drones 2023, 7(10), 595; https://doi.org/10.3390/drones7100595 - 22 Sep 2023
Cited by 3 | Viewed by 2247
Abstract
The flight formation of unmanned aerial vehicles (UAVs) needs to be reconfigured whenever necessary to cope with complex environments and varying tasks. However, the continuity, nonlinearity and high dimensionality of the UAV formation control parameters bring significant challenges to the efficiency and safety [...] Read more.
The flight formation of unmanned aerial vehicles (UAVs) needs to be reconfigured whenever necessary to cope with complex environments and varying tasks. However, the continuity, nonlinearity and high dimensionality of the UAV formation control parameters bring significant challenges to the efficiency and safety of UAV formation reconfiguration. To this end, this paper proposes a reconfiguration strategy of the UAV formation based on a modified Artificial Bee Colony (ABC) algorithm, which ensures superior efficiency and safety level simultaneously. Specifically, we first formulate the formation reconfiguration problem minimizing the time consumed for reconfiguration under the constraints of safety and connection. Then the continuous optimization problem is discretized by using the control parameterization and time discretization (CPTD) method. Finally, we use a modified ABC algorithm to find the solution of formation reconfiguration. Extensive performance evaluations are conducted to verify the superiority of the proposed method. It is concluded that the proposed algorithm achieves a better performance than the existing approaches in literature in solving the problem of 3-D formation reconfiguration. Full article
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18 pages, 12308 KiB  
Article
Multi-Objective Artificial Bee Colony Algorithm Based on Scale-Free Network for Epistasis Detection
by Yijun Gu, Yan Sun, Junliang Shang, Feng Li, Boxin Guan and Jin-Xing Liu
Genes 2022, 13(5), 871; https://doi.org/10.3390/genes13050871 - 12 May 2022
Cited by 10 | Viewed by 2490
Abstract
In genome-wide association studies, epistasis detection is of great significance for the occurrence and diagnosis of complex human diseases, but it also faces challenges such as high dimensionality and a small data sample size. In order to cope with these challenges, several swarm [...] Read more.
In genome-wide association studies, epistasis detection is of great significance for the occurrence and diagnosis of complex human diseases, but it also faces challenges such as high dimensionality and a small data sample size. In order to cope with these challenges, several swarm intelligence methods have been introduced to identify epistasis in recent years. However, the existing methods still have some limitations, such as high-consumption and premature convergence. In this study, we proposed a multi-objective artificial bee colony (ABC) algorithm based on the scale-free network (SFMOABC). The SFMOABC incorporates the scale-free network into the ABC algorithm to guide the update and selection of solutions. In addition, the SFMOABC uses mutual information and the K2-Score of the Bayesian network as objective functions, and the opposition-based learning strategy is used to improve the search ability. Experiments were performed on both simulation datasets and a real dataset of age-related macular degeneration (AMD). The results of the simulation experiments showed that the SFMOABC has better detection power and efficiency than seven other epistasis detection methods. In the real AMD data experiment, most of the single nucleotide polymorphism combinations detected by the SFMOABC have been shown to be associated with AMD disease. Therefore, SFMOABC is a promising method for epistasis detection. Full article
(This article belongs to the Section Bioinformatics)
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19 pages, 13118 KiB  
Article
An Adaptive Embedding Strength Watermarking Algorithm Based on Shearlets’ Capture Directional Features
by Qiumei Zheng, Nan Liu and Fenghua Wang
Mathematics 2020, 8(8), 1377; https://doi.org/10.3390/math8081377 - 17 Aug 2020
Cited by 14 | Viewed by 2983
Abstract
The discrete wavelet transform (DWT) is unable to represent the directional features of an image. Similarly, a fixed embedding strength is not able to establish an ideal balance between imperceptibility and robustness of a watermarked image. In this work, we propose an adaptive [...] Read more.
The discrete wavelet transform (DWT) is unable to represent the directional features of an image. Similarly, a fixed embedding strength is not able to establish an ideal balance between imperceptibility and robustness of a watermarked image. In this work, we propose an adaptive embedding strength watermarking algorithm based on shearlets’ capture directional features (S-AES). We improve the watermarking algorithm in the domain of DWT using non-subsampled shearlet transform (NSST). The improvement is made in terms of coping with anti-geometric attacks. The embedding strength is optimized by artificial bee colony (ABC) to achieve higher robustness under the premise of satisfying imperceptibility. The principle components (PC) of the watermark are embedded into the host image to overcome the false positive problem. The simulation results show that the proposed algorithm has better imperceptibility and strong robustness against multi-attacks, especially those of high intensity. Full article
(This article belongs to the Special Issue Mathematics Cryptography and Information Security)
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15 pages, 1267 KiB  
Article
An Artificial Bee Colony-Based COPE Framework for Wireless Sensor Network
by Amit Singh and Aitha Nagaraju
Computers 2016, 5(2), 8; https://doi.org/10.3390/computers5020008 - 6 May 2016
Cited by 7 | Viewed by 7913
Abstract
In wireless communication, network coding is one of the intelligent approaches to process the packets before transmitting for efficient information exchange. The goal of this work is to enhance throughput by using the intelligent technique, which may give comparatively better optimization. This paper [...] Read more.
In wireless communication, network coding is one of the intelligent approaches to process the packets before transmitting for efficient information exchange. The goal of this work is to enhance throughput by using the intelligent technique, which may give comparatively better optimization. This paper introduces a biologically-inspired coding approach called Artificial Bee Colony Network Coding (ABC-NC), a modification in the COPE framework. The existing COPE and its variant are probabilistic approaches, which may not give good results in all of the real-time scenarios. Therefore, it needs some intelligent technique to find better packet combinations at intermediate nodes before forwarding to optimize the energy and maximize the throughput in wireless networks. This paper proposes ABC-NC over the existing COPE framework for the wireless environment. Full article
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