Optimal Design of Multimissile Formation Based on an Adaptive SA-PSO Algorithm

Liu, Shuangxi; Huang, Faming; Yan, Binbin; Zhang, Tong; Liu, Ruifan; Liu, Wei

doi:10.3390/aerospace9010021

Cited by 12 publications

(7 citation statements)

References 20 publications

(27 reference statements)

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“…If f(x i ) < f (P best.i ), then set P best.i = x i. If f (x i ) > f (P best.i ), utilizing the above-mentioned simulated annealing acceptance probability p i to apply the Metropolis criterion [27]. If the probability p i is greater than a random number within the range [0, 1], then the state P best.i is still accepted.…”

Section: Simulated Annealing Particle Swarm Optimizationmentioning

confidence: 99%

Marine Radar Oil Spill Detection Method Based on YOLOv8 and SA_PSO

Xu,

Huang,

Dong

et al. 2024

JMSE

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In the midst of a rapidly evolving economic landscape, the global demand for oil is steadily escalating. This increased demand has fueled marine extraction and maritime transportation of oil, resulting in a consequential and uneven surge in maritime oil spills. Characterized by their abrupt onset, rapid pollution dissemination, prolonged harm, and challenges in short-term containment, oil spill accidents pose significant economic and environmental threats. Consequently, it is imperative to adopt effective and reliable methods for timely detection of oil spills to minimize the damage inflicted by such incidents. Leveraging the YOLO deep learning network, this paper introduces a methodology for the automated detection of oil spill targets. The experimental data pre-processing incorporated denoise, grayscale modification, and contrast boost. Subsequently, realistic radar oil spill images were employed as extensive training samples in the YOLOv8 network model. The trained detection model demonstrated rapid and precise identification of valid oil spill regions. Ultimately, the oil films within the identified spill regions were extracted utilizing the simulated annealing particle swarm optimization (SA-PSO) algorithm. The proposed method for offshore oil spill survey presented here can offer immediate and valid data support for regular patrols and emergency reaction efforts.

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Section: Simulated Annealing Particle Swarm Optimizationmentioning

confidence: 99%

Marine Radar Oil Spill Detection Method Based on YOLOv8 and SA_PSO

Xu,

Huang,

Dong

et al. 2024

JMSE

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“…Formation control of collaborative systems has been researched extensively in the literature. Typical approaches for formation control can be categorized as behavioral (Alfeo et al, 2019), leader–follower (Gong et al, 2022; Liu et al, 2022; Rosa, 2020), virtual structure (Zhou et al, 2018), and graph theory (Yan et al, 2022; Yuhang et al, 2022) based approaches. Behavioral approach requires that every action of UAV should correspond to every expected behavior of UAV, and it is defined by the weighted average of behaviors.…”

Section: Introductionmentioning

confidence: 99%

Consensus-based distributed formation control for coordinated battle system of manned/unmanned aerial vehicles

Cao

2023

Transactions of the Institute of Measurement and Control

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In this paper, a consensus-based distributed formation control method is proposed for improving the coordinated battle efficiency of coordinated battle system for manned/unmanned aerial vehicles (MAV/UAVs). The method proposed is designed based on consensus protocol, which contains three parts. First, the distributed structure with limited centralized strategy for MAV/UAVs cooperative system is designed. Second, a second-order dynamic model is built up to describe the distributed formation system, which is further simplified using feedback linearization method. Third, the information exchange topology of MAV/UAV-coordinated battle system is established on the basis of graph theory and leader–follower mechanism. Finally, the effectiveness of the proposed formation strategy for MAV/UAVs cooperative system is proved asymptotic convergence by constructing a reasonable Lyapunov function which guarantees the accurate formation maintenance. Simulation results are presented to demonstrate the effectiveness of the proposed method. And comparative simulation results reveal that the method proposed in this paper is superior to traditional algorithms in the aspects of accuracy and stability.

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“…For instance, Zha et al [12] successfully applied a genetic algorithm to the probability integral method for parameter inversion, demonstrating notable advantages in terms of accuracy and reliability. Subsequent investigations explored the use of the modular vector method [13][14][15], particle swarm algorithms [16][17][18][19], simulated annealing algorithms [20,21], and others [22][23][24][25][26][27] for parameter inversion within the probability integral method framework, all yielding highly satisfactory results. In a comparative analysis of parameter inversion outcomes using various intelligent algorithms, Han Mei et al [28] confirmed that, with an appropriate choice of initial exploration values, the modular vector method excels in accuracy and reliability when contrasted with other algorithms.…”

Section: Introductionmentioning

confidence: 99%

Research on Mining Subsidence Prediction Parameter Inversion Based on Improved Modular Vector Method

Chai,

Xu,

Guan

et al. 2023

Applied Sciences

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In this study conducted in the Shendong mining area, this paper tackles the challenge of estimating mining subsidence parameters in the absence of empirical values. The study employs a tailored pattern recognition method specifically designed for mining subsidence in a specific working face. The goal is to determine a globally approximate optimal solution for these parameters. Subsequently, this study utilizes the approximate optimal solution as an initial exploration value and harnesses the modular vector method to obtain stable, accurate, optimal solutions for the parameters. The results of the study demonstrate the effectiveness of the improved modular vector method. In simulation tests involving the subsidence coefficient, the main influence angle tangent value, the propagation angle of mining influence, and the deviation of the inflection point, the relative errors do not exceed 1.2%, 1.9%, 1.7%, and 7.9%, respectively. Furthermore, when subjected to random errors of less than 20 mm, the relative errors for each parameter remain below 2%. Even in conditions with 200 mm gross error, the relative error for each parameter does not exceed 5.1%, indicating high precision. In an engineering example, the root mean square error of the improved modular vector method’s fitting result is 64.31 mm, constituting a mere 1.79% of the maximum subsidence value. This performance surpasses that of the genetic algorithm (70.47 mm), particle swarm algorithm (72.82 mm), and simulated annealing algorithm (75.45 mm). Notably, the improved modular vector method exhibits superior stability and reduced reliance on the quantity of measured values compared to the three aforementioned algorithms. The inversion analysis of predicted parameters based on the improved modular vector method, coupled with the probability integral method, holds practical significance for enhancing the accuracy of mining subsidence prediction.

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Optimal Design of Multimissile Formation Based on an Adaptive SA-PSO Algorithm

Cited by 12 publications

References 20 publications

Marine Radar Oil Spill Detection Method Based on YOLOv8 and SA_PSO

Marine Radar Oil Spill Detection Method Based on YOLOv8 and SA_PSO

Consensus-based distributed formation control for coordinated battle system of manned/unmanned aerial vehicles

Research on Mining Subsidence Prediction Parameter Inversion Based on Improved Modular Vector Method

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