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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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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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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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Amalgamating an intelligent variant of the gravitational search algorithm with decision trees for email spam detection

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INTERNATIONAL JOURNAL OF MODERN PHYSICS C 2025年第3期36卷

作者： Kundra, Sheetal Kant, Rama Singh, Sanjiv Kumar Saini, Rupinder Gupta, Brijesh Kumar Manukonda, Vishnu Sandeep Reddy Jain, Vishal Guru Nanak Inst Technol Ibrahimpatnam Dept Comp Sci & Engn Hyderabad 501506 Telangana India GL Bajaj Grp Inst Dept Comp Sci & Engn Mathura 281406 Uttar Pradesh India Indian Inst Informat Technol Dept Comp Sci & Engn Agartala 799046 Tripura India Sharda Univ Sch Engn & Technol Dept Comp Sci & Engn Greater Noida 201310 Uttar Pradesh India

In today's interconnected digital world, email remains a primary mode of digital interaction valued for its suitability in official, academic and business communications. However, despite its utility, email faces significant challenges due to the widespread presence of spam in various forms, such as phishing, suspicious attachments and deceptive content. This issue not only affects the efficiency and security of email communication but also poses a barrier to its reliability. Therefore, it is essential to devise effective methods to tackle the escalating count of spam emails. This research work presents an intriguing methodology to combat the persistent problem of email spam. The proposed method, abbreviated as AIGSADT, is an amalgamation of the intelligent variant of the gravitational search algorithm (IGSA) and decision trees (DTs). The machine learning-based DT algorithm is individually inadequate for dealing with the large amount of constructive data on a certain attribute due to its instability and ineffectiveness. The proposed AIGSADT approach integrates the IGSA algorithm, which is effective in handling large amounts of data to detect email spam. This is achieved by constructing decision trees employing gravitational forces as the means of information transfer through mass agents. Here, the intelligent factor of the IGSA algorithm prevents the trapping of GSA agents in local optima by updating their positions based on information provided by the best and worst agents. The performance of the presented AIGSADT approach is analyzed through experiments conducted on various categories available in the Ling spam dataset. These experimental evaluations aim to analyze the significance of different pre-processing modules across different dataset categories. The comparative analysis indicates the supremacy of the proposed approach compared to state-of-the-art methodologies.

关键词： Decision tree email spam detection gravitational search algorithm machine learning metaheuristic intelligent gravitational search algorithm physics-inspired computation algorithm

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Microgrid energy management with renewable energy using gravitational search algorithm

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ELECTRICAL ENGINEERING 2025年第3期107卷 3761-3774页

作者： Kumar, T. Praveen Ajith, K. Srinivas, M. Kumar, G. Sunil Kakatiya Inst Technol & Sci Warangal Dept Elect & Elect Engn Warangal Telangana India

The microgrid energy management with renewable energy is efficiently integrating intermittent sources like solar and wind while ensuring grid stability and reliability is difficult. The gravitational sear search method is employed in MG energy management with renewable energy sources (RESs) to address these problems. The gravitational search technique is used in the proposed method (GSA). In order to build a database of control signals that take into account the power differential between the source and load sides, GSA is used to precisely identify the control signals for the system. The proposed technique's main goal is to deliver the best performance at the lowest possible cost. The constraints are the availability of the RESs, energy consumption as well as the storage elements' level of charge. Batteries are utilized as an energy source to steady and allow the renewable power system components to continue operating at a constant and stable output power. The proposed method cost is 1.1$ that is lower than the existing methods. The MATLAB platform is used to implement the proposed method, and its efficacy is assessed in comparison to established techniques like modified PSO (MPSO), genetic algorithm (GA), particle swarm optimization (PSO), and proportional integral controller (PI) (MPSO).

关键词： Renewable energy sources Energy management gravitational search algorithm Optimization strategy Batteries Load variation

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Global optimization of magnetotelluric data via an improved gravitational search algorithm

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EARTH SCIENCE INFORMATICS 2025年第2期18卷 1-18页

作者： Li, Dewei Wang, Xiangpeng China Geol Survey Chengdu Ctr Geosci Innovat Ctr Southwest China Chengdu Peoples R China Chengdu Univ Technol Coll Geophys Chengdu Peoples R China

This paper presents an improved gravitational search algorithm (GSA) for the one-dimensional magnetotelluric (MT) inversion. The inversion of MT data is inherently ill-posed and nonlinear. Traditional deterministic inversion methods, which rely on approximate linearized solutions, are highly sensitive to the initial model and are prone to converging to local optimal solutions. The GSA is a novel metaheuristic algorithm inspired by the law of gravity and mass interactions to deal with nonlinear optimization problems. It is improved through hybrid methods involving population initialization and calculation of gravitational force, and it is employed for one-dimensional MT inversion to search for the global optimum solution. Using synthetic data derived from two geoelectric models, the effectiveness of main parameters in GSA for MT inversion was investigated. The inversion results from the improved GSA were comparable to the true models and demonstrated robustness, as evidenced by repeatability and noise tests. Additionally, it also shows good agreement with previous results obtained using other methods when dealing with the field MT dataset COPROD2. Therefore, both synthetic and field data tests demonstrate that the improved GSA has good performance for MT inversion. In particular, the improved GSA is suitable for inverting MT data and does not require an initial model.

关键词： gravitational search algorithm Global optimization Magnetotelluric Inversion

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Quantum behaved binary gravitational search algorithm with random forest for twitter spammer detection

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RESULTS IN ENGINEERING 2025年 25卷

作者： Sharma, Kanta Prasad Lal, Gendal Shukla, Madhu Yadav, Anupam Jayaprakash, B. Juneja, Bhanu Jagtap, Jayant Singh, Amrita Bhowmik, A. Santhosh, A. Johnson Amity Univ Amity Sch Engn & Technol Dept CSE Greater Noida Campus Greater Noida Uttar Pradesh India Vivekanand Global Univ Dept Comp Sci & Engn Jaipur India Marwadi Univ Res Ctr Fac Engn & Technol Dept Comp Engn Rajkot 360003 Gujarat India Graphic Era Hill Univ Dept Comp Sci & Engn Dehra Dun India JAIN Deemed Univ Sch Sci Dept Comp Sci & IT Bangalore Karnataka India Chitkara Univ Inst Engn & Technol Ctr Res Impact & Outcome Rajpura 140401 Punjab India NIMS Univ Rajasthan NIMS Sch Comp Sci & Artificial Intelligence Jaipur India Chandigarh Engn Coll Coll Jhanjeri Dept Comp Sci & Engn Chandigarh Grp Mohali 140307 Punjab India Chitkara Univ Chitkara Ctr Res & Dev Baddi 174103 Himachal Prades India Jimma Univ Jimma Inst Technol Fac Mech Engn Jimma Ethiopia Graphic Era Deemed Univ Dehra Dun 248002 Uttarakhand India

The emergence of social media platforms like Twitter has significantly changed the landscape of communication by increasing accessibility for widely disseminating official announcements, professional interactions, and important news in real-time. Despite these advantages, the prevalence of spammers and their spamming activities is increasing regularly. To mitigate the growing number of spammers, it is essential to develop an efficient and robust method for Twitter spammer detection. This research presents a novel QBGSRF method by combining the quantum-behaved binary gravitational search algorithm (QBGSA) with random forest (RF) for timely detection of Twitter spammers. The QBGSA algorithm adds the characteristics of quantum computing (QC) and binary gravitational search algorithm (BGSA), which enables the quantum agents to quickly determine solutions using the superposition attributes of QC and the position update via bit-flipping based on velocity probabilities of the BGSA algorithm. In the proposed QBGSRF method, the quantum agents utilize the aforementioned attributes and the principles of the RF algorithm to construct the decision trees for effectively detecting Twitter spammers. The proposed method is assessed for the datasets of 1KS-10KN and Social Honeypot. In order to access the efficacy of the proposed method, the results are also evaluated using the BGSRF method (a combination of BGSA and RF algorithm) and RF algorithm. The experimental evaluations indicate that the proposed method outperforms the aforementioned and state-of-the-art methods.

关键词： Twitter Twitter spammer detection gravitational search algorithm Quantum computing Binary gravitational search algorithm Random forest Machine learning Metaheuristic

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MSGGSA: a multi-strategy-guided gravitational search algorithm for gene selection in cancer classification

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PATTERN ANALYSIS AND APPLICATIONS 2025年第2期28卷 1-30页

作者： Li, Min Jin, Chen Cai, Yuheng Deng, Shaobo Wang, Lei Nanchang Inst Technol Sch Informat Engn 289 Tianxiang Rd Nanchang Jiangxi Peoples R China

Microarray gene expression data, widely employed in the field of cancer research, provides valuable insights for distinguishing between various types of cancers. However, the inherent challenges associated with microarray data, such as limited sample size and high dimensionality, complicate the process of cancer classification. To effectively address these challenges, swarm intelligence optimization techniques have been employed for gene selection from microarray gene expression datasets. Nevertheless, accurately classifying cancer types remains a formidable challenge. The present study presents a gene selection method for cancer classification, employing a multi-strategy-guided gravitational search algorithm (MSGGSA). In MSGGSA, the traditional gravitational search algorithm (GSA) is enhanced by incorporating strategies such as segmented population initialization, inertia stagnation, dynamic update of gravitational constant, and relearning strategy for elite individuals. The proposed enhancements effectively address the limitations associated with excessive randomness in the initial population, premature convergence susceptibility, and vulnerability to local optima encountered by traditional GSA. Moreover, this enhancement effectively achieves a balance between exploration and exploitation within algorithm. The superior performance of the proposed algorithm on high-dimensional data is demonstrated through rigorous testing on 12 publicly available microarray datasets, highlighting its superiority over other popular swarm intelligence algorithms and current state-of-the-art methods.

关键词： Cancer classification Microarray gene expression data gravitational search algorithm Gene selection Swarm intelligence algorithms

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Multi-objective optimization of hybrid energy systems using gravitational search algorithm

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SCIENTIFIC REPORTS 2025年第1期15卷 1-20页

作者： Mahmoudi, Sayyed Mostafa Maleki, Akbar Ochbelagh, Dariush Rezaei Amirkabir Univ Technol Tehran Polytech Dept Energy Engn & Phys Tehran Iran Shahrood Univ Technol Fac Mech Engn Shahrood Iran

The depletion of fossil fuel reserves, increasing environmental concerns, and energy demands of remote communities have increased the acceptance of using hybrid renewable energy systems (HRES). However, choosing an optimal HRES from economic, environmental, reliability, and sustainability aspects is still challenging. To solve this challenge, this study introduces a novel multi-objective optimization approach using the gravitational search algorithm (GSA) and non-dominated sorting techniques. The proposed framework addresses four objectives: minimizing the loss of power supply probability, reducing total costs, increasing renewable energy fraction, and lowering CO2 emissions. A carbon tax sensitivity analysis evaluates the system's economic performance under varying scenarios. Also, the amount of damage caused by the release of carbon dioxide on human health and the ecosystem is examined. In this way, an optimal configuration consisting of wind turbines, photovoltaic panels, and diesel generators is introduced to satisfy the above objectives. Results demonstrate that the GSA outperforms established methods, such as multi-objective particle swarm optimization and non-dominated sorting genetic algorithm II in Pareto front diversity and convergence. In this work, the optimal system achieves an 18.4% increase in renewable energy share, reducing ecosystem and human health damage by 14.2%. Notably, with a 20% increase in the carbon tax, system costs increased by 3%. These findings underscore the potential of multi-objective optimization combined with carbon tax policies to enhance energy system sustainability and affordability.

关键词： Hybrid renewable energy system Multi-objective optimization gravitational search algorithm Sustainable energy Carbon tax

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