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gravitational search algorithm Swarm-Based UAV Reconnaissance for Multiple Targets Detection in Unknown Environment

IEEE ACCESS 2025年 13卷 36897-36908页

作者： Al Amin, Ahmed Azam, Irfan Masuduzzaman, Md Qayyum, Abdullah Sarwar, Muhammad Sajid Khan, Saud Shin, Soo Young RFS Technol Meriden CT 06450 USA Polytech Montreal Dept Elect Engn Montreal PQ H3T 0A3 Canada McMaster Univ DeGroote Sch Business Hamilton ON L8S 4M4 Canada Univ Sussex Sch Engn & Informat 6G Lab Brighton BN1 9RH E Sussex England Kumoh Natl Inst Technol Dept IT Convergence Engn Gumi Si 39176 South Korea Australian Natl Univ Sch Engn Canberra ACT 2601 Australia

Target detection in an unknown environment is a crucial aspect of reconnaissance using a swarm of unmanned aerial vehicles (UAVs). An efficient target detection technique is required to minimize the number of iterations for searching and maximize the coverage area with respect to the number of iterations and detected targets. This paper proposes a gravitational search algorithm (GSA) swarm-based UAV reconnaissance scheme to detect targets in an unknown environment. Additionally, different GSA-based searching methods are analyzed to identify the most efficient one with the minimum number of iterations and maximum coverage. Extensive simulations are performed, and the results of the proposed scheme are compared with existing search schemes. The results demonstrate that the proposed GSA swarm-based detection scheme requires fewer iterations and provides greater area coverage than existing UAV reconnaissance schemes for target detection in an unknown environment.

关键词： Drones Reconnaissance Optimization Object detection Heuristic algorithms Autonomous aerial vehicles search problems Surveillance Probabilistic logic Biology UAV reconnaissance gravitational search algorithm swarm unknown environment

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gravitational search algorithm-based optimization of hybrid wind and solar renewable energy system

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COMPUTATIONAL INTELLIGENCE 2022年第3期38卷 1106-1132页

作者： Geleta, Diriba Kajela Manshahia, Mukhdeep Singh Punjabi Univ Dept Math Patiala Punjab India Madda Walabu Univ Dept Math Addis Ababa Ethiopia

Due to the issue of environmental protection coupled with high energy demand, there was an initiation for exploration of different renewable energy sources. This article aims to optimize the total annual cost of hybrids of wind and solar renewable energy system to satisfy the predesigned load. Minimization of the total annual cost of the system by determining appropriate numbers of the components, so that the desired load can be economically and reliably satisfied under the given constraints. gravitational search algorithm (GSA) was employed for the optimization process. GSA is a recently proposed metaheuristic algorithm which is based on Newton's universal gravitational law of gravity and mass interactions. It uses stochastic rules to escape local optima and find the global optimal solutions. MATLAB codes were designed for the developed fitness function and employed algorithm. The proposed methodology was run for the fitness function through the code and the results were discussed. The result was compared with the results of Particle Swarm Optimization (PSO) and also shown that: GSA has some advantage over PSO algorithm. Even though, the algorithm has several parameters to be adjusted, it is strong in both local and global optimal searches.

关键词： gravitational search algorithm hybrid renewable energy nature inspired algorithm optimization

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gravitational search algorithm based strategy for combinatorial t-way test suite generation

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JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 2022年第8期34卷 4860-4873页

作者： Htay, Khin Maung Othman, Rozmie Razif Amir, Amiza Alkanaani, Jalal Mohammed Hachim Univ Malaysia Perlis Fac Elect Engn Technol Arau 02600 Perlis Malaysia Univ Malaysia Perlis Adv Commun Engn ACE Ctr Excellence Kangar 01000 Perlis Malaysia Iraq Univ Coll Dept Comp Engn Basra Iraq

Due to different users' requirements, contemporary software has become feature-rich in terms of input functions (i.e., parameters) and selections (i.e., values). Exhaustive testing on sophisticated software systems is impractical as far as testing time and cost are concerned. Various test case design strategies have been proposed in the literature, such as equivalence class partitioning, boundary value analysis and decision tables. Unlike earlier works, combinatorial t-way testing supports the detection of faults caused by two or more input parameter interactions and thus efficiently minimizes the size of the test suite. Over the past few years, metaheuristic algorithms have appeared to be the most common choice for researchers since their effectiveness proves to offer optimal/near-optimal results. However, generating a t-way test suite is an NP-hard problem, and no single t-way strategy can guarantee to show superiority to others for all types of system configurations. Hence, this paper presents a new t-way strategy based on the gravitational search algorithm (GSA), known as the gravitational search Test Generator (GSTG). The primary contribution of this paper is that GSA has adapted for the first time to t-way test data generation. The benchmarking results showcase that GSTG obtains competitive results in most system configurations compared to other existing strategies and addresses higher combination coverage (i.e., t <= 10). (C) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University.

关键词： Combinatorial testing T-way test suites Interaction testing Metaheuristic gravitational search algorithm

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CMACGSA: Improved gravitational search algorithm Based on Cerebellar Model Articulation Controller for Optimization

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IEEE ACCESS 2025年 13卷 20847-20870页

作者： Bulut, Nazmiye Ebru Dandil, Emre Yuzgec, Ugur Duysak, Alpaslan Bilecik Seyh Edebali Univ Inst Grad Dept Elect & Comp Engn TR-11230 Bilecik Turkiye Konya Tech Univ Distance Educ Applicat Res Ctr TR-42250 Konya Turkiye Bilecik Seyh Edebali Univ Fac Engn Dept Comp Engn TR-11230 Bilecik Turkiye Texas A&M Univ Dept Comp Sci & Engn College Stn TX 77843 USA

Metaheuristic algorithms have gained significant attention in recent years for addressing complex and challenging optimization problems, especially in engineering. These algorithms often take inspiration from natural phenomena, systems or biological behaviour to find optimal solutions. Recent advances in the field often involve hybrid methods that combine several algorithms to improve performance. This study introduces an improved gravitational search algorithm, named CMACGSA, which incorporates the Cerebellar Model Articulation Controller (CMAC)-a neural network model-to enhance the performance of gravitational search algorithm (GSA). By employing the CMAC neural network, CMACGSA dynamically learns the masses of particles/agents of GSA, enabling a learning-driven approach to mass computation. Additional enhancements include L & eacute;vy mutation, boundary control methods and an error handling mechanism, which together improve the robustness and adaptability of the algorithm. The effectiveness of CMACGSA is demonstrated through extensive testing on a set of 2D CEC 2014 benchmark functions, where it significantly outperforms the original GSA. Further evaluations on multidimensional CEC 2014 test problems, including 30-dimensional cases, reveal improved performance over widely used optimization algorithms and state-of-the-art (SOTA) algorithms. Furthermore, CMACGSA consistently achieves top-tier average performance metrics when benchmarked against four well-established GSA variants. The applicability of the algorithm is further validated by engineering design problems where it demonstrates outstanding performance, confirming its value in solving complex engineering challenges.

关键词： Optimization hybrid optimization methods hybrid optimization methods metaheuristic algorithms metaheuristic algorithms gravitational search algorithm gravitational search algorithm cerebellar model articulation controller cerebellar model articulation controller engineering optimization engineering optimization

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COVID-19 chest CT scan image segmentation based on chaotic gravitational search algorithm

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EVOLVING SYSTEMS 2025年第1期16卷 1-33页

作者： Rather, Sajad Ahmad Das, Sujit Ciftcioglu, Aybike Ozyuksel Natl Inst Technol Warangal Dept Comp Sci & Engn Hanamkonda 506004 Telangana India Manisa Celal Bayar Univ Fac Engn Dept Civil Engn TR-45140 Manisa Turkiye

Image segmentation is a pivotal phase in the image processing pipeline, offering detailed insights into various image features. Traditional segmentation methods grapple with challenges such as local minima and premature convergence when navigating intricate pixel search spaces. Additionally, these algorithms experience prolonged processing times as the number of threshold levels increases. To mitigate these issues, we implemented the Chaotic gravitational search algorithm (CGSA), a robust optimizer, for the multi-level thresholding of COVID-19 chest CT scan images. CGSA amalgamates the gravitational search algorithm (GSA) for exploration with chaotic maps for exploitation. Kapur's entropy method was employed to partition the sample images based on optimal pixel values. The segmentation performance of CGSA was rigorously assessed on various COVID-19 chest CT scan imaging datasets from Kaggle, utilizing metrics such as Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM). The qualitative analysis encompassed convergence curves, segmented graphs, colormap images, histogram curves, and boxplots. Statistical validation was conducted using the signed Wilcoxon rank sum test, and eight sophisticated heuristic algorithms were enlisted for comparative analysis. The comprehensive evaluation unequivocally demonstrated CGSA's superiority in terms of processing time efficiency and its ability to provide optimal values for image quality metrics, establishing it as a powerful tool for quickly assessing COVID-19 disease severity.

关键词： gravitational search algorithm Chaos theory Image segmentation Medical imaging COVID-19 Kapur's entropy

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gravitational search algorithm based Long Short-term Memory Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction with Uncertainty

Gravitational Search Algorithm based Long Short-term Memory ...

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2023 IEEE International Conference on Energy Technologies for Future Grids, ETFG 2023

作者： Reza, M.S. Hannan, M.A. Mansor, M. Ker, Pin Jern Tiong, Sieh Kiong Hossain, M.J. Universiti Tenaga Nasional Coe Department of Electrical and Electronic Engineering Kajang43000 Malaysia Sunway University School of Engineering and Technology Petaling Jaya Bandar Sunway47500 Malaysia Universiti Tenaga Nasional Institute of Sustainable Energy Kajang43000 Malaysia University of Technology Sydney School of Electrical and Data Engineering UltimoNSW2007 Australia

ISBN: (纸本)9781665471640

This paper presents a hybrid approach for predicting the remaining useful life (RUL) and future capacity of lithium-ion batteries (LIBs) using an improved long short-term memory (LSTM) deep neural network with a gravitational search algorithm (GSA). The proposed method address the challenges of nonlinear and dynamic battery behavior, battery aging uncertainty, the requirement for optimal hyperparameters tuning, and the importance of maintaining safe and efficient battery operation. The RUL prediction uncertainty with a 95% confidence interval (CI) is also analyzed. The GSA algorithm optimizes the hyperparameters of the LSTM network to construct an optimal model. The method proposed in this work is evaluated based on the aging data from the NASA battery dataset, and its effectiveness is compared with that of BiLSTM, baseline gated recurrent unit (GRU), and baseline LSTM using various error metrics. The results demonstrate that the LSTM-GSA model outperforms other methods in the context of prediction accuracy, achieving a minimum RMSE of 1.04% and 1.15% for both battery cases. Overall, this research provides a promising solution for predicting RUL and the future capacity of LIBs with uncertainty, which is essential for ensuring the safe and efficient operation of energy storage systems. © 2023 IEEE.

关键词： capacity prediction deep neural network gravitational search algorithm lithium-ion batteries long short-term memory remaining useful life

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Memetic gravitational search algorithm with Hierarchical Population Structure

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2025年第2期E108A卷 94-103页

作者： Dong, Shibo Li, Haotian Yang, Yifei Yu, Jiatianyi Lei, Zhenyu Gao, Shangce Univ Toyama Fac Engn Toyama 9308555 Japan Hirosaki Univ Fac Sci & Technol Hirosaki 0368560 Japan

The multiple chaos embedded gravitational search algorithm (CGSA-M) is an optimization algorithm that utilizes chaotic graphs and local search methods to find optimal solutions. Despite the enhancements introduced in the CGSA-M algorithm compared to the original GSA, it exhibits a pronounced vulnerability to local optima, impeding its capacity to converge to a globally optimal solution. To alleviate the susceptibility of the algorithm to local optima and achieve a more balanced integration of local and global search strategies, we introduce a novel algorithm derived from CGSA-M, denoted as CGSA-H. The algorithm alters the original population structure by introducing a multi-level information exchange mechanism. This modification aims to mitigate the algorithm's sensitivity to local optima, consequently enhancing the overall stability of the algorithm. The effectiveness of the proposed CGSA-H algorithm is validated using the IEEE CEC2017 benchmark test set, consisting of 29 functions. The results demonstrate that CGSA-H outperforms other algorithms in terms of its capability to search for global optimal solutions.

关键词： hierarchical population structure memetic algorithms meta- heuristic algorithms gravitational search algorithm

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Entropy driven integrated fractional particle swarm optimization - gravitational search algorithm optimization expedition for optimal coordination of directional over current relays

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 124卷

作者： Iqbal, Asim Muhammad, Yasir Awan, Saeed Ehsan Khan, Bakht Muhammad Raja, Muhammad Asif Zahoor COMSATS Univ Islamabad Dept Elect Engn Attock Campus Attock Pakistan Air Univ Islamabad Dept Elect Engn Kamra Campus Kamra Pakistan COMSATS Univ Islamabad Dept Comp Engn Attock Campus Attock Pakistan Natl Yunlin Univ Sci & Technol Future Technol Res Ctr 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan

The protective system is an essential part of all power network subsystems, including the protection systems of generation, transmission, and distribution networks, in order to ensure the integrity of the power system components, such as generators, bus bars, transformers, and feeder lines thus, a combination of different types of protection relays is utilized in the protection system, i.e., the prevention of the overcurrent, line to ground, line to line, double line to ground, faults in the associated system. In the current study, the performance of legacy power system protection is enhanced by means of reducing the total time of operation, including the directional over current relay (DOCR) operating time and coordination time among primary and backup DOCRs, while keeping the coordination time of interval (CTI), pickup tap setting (PTS), and time dial setting (TDS) within acceptable limits, during the protection of standard power system. In order to reduce the fitness evaluation function in IEEE 3-bus, 8-bus, and 15-bus systems, a new approach called fractional particle swarm optimization gravitational search algorithm entropy metric (FPSOGSA-EM) is designed for determining the optimal settings of the CTI, PTS, and TDS. The FPSOGSA-EM incorporates the underlying theories of fractional derivatives inside the mathematical framework of canonical particle swarm optimization aided with gravitational search algorithm along with entropy metric to improve its convergence rate and avoid sub optimality. The yielded results from FPSOGSAEM are compared to those from other cutting-edge counterpart algorithms such as modified particle swarm optimization, modified water cycle technique, modified electromagnetic field optimization, enhanced grey wolf optimization, seeker algorithm, teaching learning-based optimization, harmony search algorithm and FPSOGSA. By sharply reducing the period of operation of DOCRs in traditional IEEE 3-bus, 8-bus, and 15-bus test systems, the FPSOGSA-E

关键词： Directional overcurrent relay fractional particle swarm optimization gravitational search algorithm entropy metric coordination time of interval pickup tap setting time dial setting

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Novel discretized gravitational search algorithm for effective medical hyperspectral band selection

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2024年第18期361卷

作者： Zhang, Chenglong Ma, Xiaopeng Zhang, Aizhu Yan, Bin Zhao, Kai Cheng, Qiyuan Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China Shandong First Med Univ Shandong Prov Hosp Jinan 250021 Peoples R China Shandong First Med Univ Shandong Prov Hosp Med Engn Dept Jinan 250021 Peoples R China

Medical hyperspectral imaging present a promising avenue for non-invasive diagnostic methods for diseases. Nonetheless, the sparsity of medical hyperspectral data within high-dimensional spaces introduces the "curse of dimensionality", which diminishes the efficiency and accuracy of data processing efforts. Therefore, spectral dimensionality reduction emerges as an essential process in the analysis and utilization of MHSIs data. To retain the intrinsic properties of the spectral bands, an effective unsupervised band selection algorithm is proposed leveraging the gravitational search algorithm (GSA-UBS) to identify the optimal band subset. Taking into account the informational content and redundancy among candidate bands, a comprehensive evaluation criterion is established that incorporates a band distance matrix and an information entropy vector. Additionally, a straightforward discrete search strategy is developed that enables gravitational search algorithm to directly retrieve the original sequence numbers of the selected bands, bypassing the conventional 0-1 band weighting approach. The extensive evaluation of GSA-UBS on three publicly available invivo brain cancer MHSIs datasets and a remote sensing hyperspectral image demonstrates its superior performance compared to various stateof-the-art methods. The source code for GSA-UBS can be accessed at https://***/ zhangchenglong1116/GSA_UBS.

关键词： gravitational search algorithm Medical hyperspectral images Band selection Dimensionality reduction

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Application of dynamic event-triggered hybrid gravitational search algorithm in pipeline leakage detection

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2024年第4期361卷

作者： Yang, Dandi Lu, Jingyi Dong, Hongli Li, Jiahui Zhou, Yina Northeast Petr Univ Artificial Intelligence Energy Res Inst Daqing 163318 Peoples R China Northeast Petr Univ SANYA Offshore Oil & Gas Res Inst Sanya 572024 Peoples R China Northeast Petr Univ Heilongjiang Prov Key Lab Networking & Intelligent Daqing 163318 Peoples R China

In this paper, a hybrid gravitational search algorithm (GSA) based on the dynamic eventtriggered mechanism, which is called DHGSA, is proposed to alleviate the slow exploitation problem of the GSA that may result in premature convergence and falling into local optimization. First, the DHGSA is divided into the exploration stage and the exploitation stage through the introduction of the population diversity. Then, the memory and social information exchange abilities of the particle swarm optimization algorithm are added in the DHGSA's exploration stage, which can accelerate the convergence speed of the GSA. Next, the dynamic eventtriggered mechanism (DETM) is added in the exploitation stage of the DHGSA, and a "celestial banishment strategy"is proposed, which improves the exploitation ability of the GSA and makes it have the ability to avoid falling into local optimal value. The derived results indicate that, compared with several other well-known search algorithms, the DHGSA has better performance on convergence speed, optimization accuracy and stability. Finally, the DHGSA is employed to optimize the parameters of the variational mode decomposition (VMD) algorithm for the oil and gas pipeline signal decomposition. The signal-to-noise ratio (SNR) of the three types of pipeline signals after denoising reaches 8.8406 dB, 9.0869 dB and 5.6880 dB, respectively. It is proved that the leakage signals can be denoised effectively in the research of the oil and gas pipeline signal processing.

关键词： Pipeline leakage detection gravitational search algorithm Celestial banishment strategy Dynamic event-triggered mechanism Variational mode decomposition

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