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Optimizing feature selection and remote sensing classification with an enhanced machine learning method

JOURNAL OF SUPERCOMPUTING 2025年第2期81卷 1-43页

作者： Ewees, Ahmed A. Alshahrani, Mohammed M. Alharthi, Abdullah M. Gaheen, Marwa A. Univ Bisha Coll Comp & Informat Technol Dept Informat Syst & Cybersecur POB 551 Bisha 61922 Saudi Arabia Damietta Univ Dept Comp Dumyat Egypt

In the era of rapidly expanding data volumes, the increasing dimensionality of features presents significant computational challenges, often degrading the performance of algorithms. Feature selection has emerged as a critical pre-processing step across various applications, aiming to identify and retain the most relevant features from datasets to enhance efficiency and accuracy. This study introduces an advanced wrapper-based feature selection approach, addressing key limitations of the original grasshopper optimization algorithm (GOA), such as premature convergence and entrapment in local optima. The proposed grasshopper optimization algorithm Harris Hawks Optimizer L & eacute;vy Flight (GHL) integrates two strategies: L & eacute;vy flight, which enhances the exploration phase by directing GOA toward promising regions of the search space, and Harris Hawks Optimizer techniques, which strengthen the exploitation phase to improve solution quality. Through three comprehensive experiments, the GHL algorithm demonstrated superior performance over nine comparative methods. The first experiment validated its efficacy in solving global optimization problems, achieving the best fitness values in most of the test functions. The second experiment highlighted its ability to effectively select relevant features across twenty benchmark datasets, achieving the best accuracy in 80% of the datasets. The third experiment applied GHL to remote sensing image classification, improving classification accuracy and yielding robust optimization outcomes.

关键词： Feature selection Remote sensing grasshopper optimization algorithm Harris hawks optimizer L & eacute vy flight

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Probabilistic optimization of grinding processes in manufacturing industry

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2025年第10期136卷 4525-4534页

作者： Ferhat, Hamza Liang, Gao Djeddou, Ferhat Setif 1 Univ Inst Opt & Precis Mech Appl Precis Mech Lab Setif 19000 Algeria Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

Deterministic optimization of machining processes in the manufacturing industry usually leads to suboptimal results with a high failure probability. This is due to the uncertainty and random variation of the input data which can be derived from diverse sources. Therefore, the purpose of this research work is to introduce a probabilistic optimization (PO) for handling manufacturing processes in the presence of uncertainties. First, a new PO approach (ESMV-GOA) is developed based on integrating the strategy of enriched self-adjusted mean value (ESMV) into the grasshopper optimization algorithm (GOA). Then, the proposed approach is applied to select the optimal machining parameters of a well-known grinding optimization problem. The obtained results indicate that the ESMV-GOA is a competent tool for optimizing manufacturing problems while guaranteeing the desired reliability level.

关键词： Probabilistic optimization Manufacturing Grinding operation Enriched self-adjusted mean value grasshopper optimization algorithm

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Workflow task scheduling in a cloud-fog environment: a hybrid PSO-GOA approach

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2025年 1-23页

作者： Bansal, Sumit Singla, Bhim Sain Aggarwal, Himanshu Thapar Inst Engn & Technol Dept Comp Sci & Engn Patiala 147004 Punjab India Punjabi Univ Dept Comp Sci Neighbourhood Campus Ralla 151510 Punjab India Punjabi Univ Dept Comp Sci & Engn Patiala 147002 Punjab India

Cloud-fog computing frameworks represent emerging paradigms designed to enhance existing Internet of Things (IoT) architectures. In these frameworks, task scheduling is crucial for optimizing task allocation and execution within the cloud-fog computing environment. Finding an optimal algorithm for workflow scheduling poses a significant challenge due to the complex nature and variable aspects of the tasks and resources involved. Metaheuristic algorithms are the ones which can overcome this problem and also offers a variety according to the nature of problem. However, they frequently encounter issues such as getting trapped in local optima, which delays their ability to attain the global optimal solution. So, as to eliminate this issue, we employed a hybrid approach known as the "Hybrid Particle Swarm optimization algorithm-grasshopper optimization algorithm" (HPSO-GOA). By leveraging the characteristics of both PSO and the GOA, the proposed algorithm adeptly addresses the issue of becoming trapped in local optima. The objective of this work is to minimize the Total Execution Time (TET), Total Execution Cost (TEC), and Energy Consumption (EC). Based on the evaluation metrics, our Multiobjective optimization algorithm outperforms in terms of TET achieving an overall average reduction of 6.69% for PSO and 20.60% for the GOA and 1.65% for Hybrid Genetic algorithm-Particle Swarm optimization (HGA-PSO). When compared for TEC and EC it outperforms PSO by 3.29%, 10.84%, GOA by 16.46%, 17.85% and HGA-PSO by 4.40%, 3.27% respectively. To evaluate the effectiveness of the proposed algorithm, we conducted comparative analyses with state-of-the-art algorithms (PSO, GOA, HGA-PSO) across five diverse scientific workflows. These comparative analyses and statistical analysis (Wilcoxon and Friedman) highlighted the effectiveness of the HPSO-GO algorithm in enhancing workflow scheduling performance.

关键词： Cloud Fog Scheduling Workflow Hybridization optimization grasshopper optimization algorithm

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A new hybrid optimization technique based on antlion and grasshopper optimization algorithms

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EVOLUTIONARY INTELLIGENCE 2023年第4期16卷 1383-1422页

作者： Amaireh, Anas Atef Al-Zoubi, Asem S. Dib, Nihad, I Univ Oklahoma Sch Elect & Comp Engn Norman OK 73071 USA Yarmouk Univ Telecommun Engn Dept POB 35 Irbid 21163 Jordan Jordan Univ Sci & Technol Elect Engn Dept POB 3030 Irbid 22110 Jordan

This paper proposes a new hybrid algorithm that merges the main features of two well-known metaheuristic algorithms;grasshopper optimization algorithm (GOA) and Antlion optimization (ALO) algorithm. ALO is strong in exploitation due to the mechanism of antlions in hunting other insects. On the other hand, the social forces in GOA represent the strong capability of exploration all over the search space. So, these features give the chance to combine ALO and GOA in one hybrid algorithm that significantly enhances the performance of both methods. The proposed hybrid algorithm is tested on 32 well-known benchmark test functions, 13 functions of the challenging CEC2015 functions, and two real problems in antenna array synthesis where the elements' excitation amplitudes and phases are optimized to minimize the maximum sidelobe level and impose nulls at specific angles. Comparisons show that the proposed algorithm outperforms 18 well-known optimization methods, including ALO and GOA, in the majority of these tests, with huge differences in some of them, which prove the stability, robustness, and efficiency of the proposed method over other robust algorithms.

关键词： optimization Metaheuristics algorithms Evolutionary algorithms Benchmark algorithm Antlion optimization algorithm grasshopper optimization algorithm

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Eel and Grouper Optimizer improvement three-stage algorithm for photovoltaic MPPT

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RESULTS IN ENGINEERING 2025年 25卷

作者： Zhou, Zijian Fang, Yanhong Liaoning Tech Univ Coll Elect & Engn Control Xingcheng 125100 Peoples R China

Photovoltaic power generation system is extremely sensitive to the change of illumination, when it is obscured by dust, cloud shadows, etc., it will produce a power voltage characteristic curve with multi-peak characteristics, and the traditional Maximum Power Point Tracking (MPPT) technology will fall into the local optimal solution when it meets multiple peaks. In order to solve this problem, the latest intelligent optimization algorithm, Eel and Grouper Optimizer (EGO), is used to integrate chaos mapping, grasshopper optimization algorithm (GOA) and other improved methods on the basis of the original algorithm. Firstly, the chaotic mapping is used to initialize the distribution of particles to improve the search performance. After the EGO as the main algorithm completed the first position update, the GOA was used to redistribute the particles, and finally the tumbling formula was introduced to further improve the ability of particles to jump out of the local optimal solution. Finally, the data of simulation experiment proves that: In the three groups of light comparison experiments, the tracking efficiency reached 94.03 % in the constant light experiment, the tracking efficiency of the two-stage variable light experiment is more than 98 %, and the comprehensive tracking performance of the three-stage variable light experiment is the best compared with other algorithms, and no large power loss is generated in the tracking process, and the curve is relatively stable. By comparing the graph with the data, it shows that the improved Eel and Grouper Optimizer used in this paper has good performance and prospect in the application of Maximum Power Point Tracking.

关键词： Eel and Grouper Optimizer grasshopper optimization algorithm Chaotic mapping Adaptive parameter Maximum power point tracking Photovoltaic power generation

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Ground Settlement Prediction in Urban Tunnelling: Leveraging Metaheuristic-Optimized Random Forest Models

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年 1-20页

作者： Yang, Peixi Zhou, Jian Zhang, Yulin Xu, Chengpei Khandelwal, Manoj Huang, Shuai Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China Univ New South Wales Sch Minerals & Energy Resources Engn Sydney Australia Federat Univ Australia Inst Innovat Sci & Sustainabil Ballarat Australia

With the continuous acceleration of urbanization, the problem of ground settlement induced by underground tunnel construction has received more and more widespread attention. This study addresses the challenge of predicting surface subsidence in urban tunnel construction, a critical concern in geotechnical engineering. Random forest (RF) models were optimized using three distinct metaheuristic algorithms: ant lion optimizer (ALO), multiverse optimizer (MVO), and grasshopper optimization algorithm (GOA). The enhancements significantly improved model accuracy, as demonstrated by the detailed performance metrics of the GOA-optimized RF model (GOA-RF Pop = 20) on the Changsha Metro Line 3 dataset, which included 294 instances and 12 feature parameters. This model achieved an MAE of 1.3820, MAPE of 181.2249, correlation coefficient of 0.9273, and RMSE of 2.5209 in the training set;and an MAE of 2.4695, MAPE of 275.2054, R value of 0.8877, and RMSE of 4.2540 in the testing set. A sensitivity analysis within the random forest framework revealed that torque (To) and ground condition (Gc) had the most significant impact on predicting surface subsidence, whereas the influence of the modified dynamic penetration test (MDPT) was the least pronounced. Additionally, a MATLAB-based application was developed using the App Designer module, integrating these models into a user-friendly GUI that facilitates the prediction and management of surface subsidence risks, thereby enhancing the practical application and effectiveness of engineering risk mitigation strategies.

关键词： Machine learning Random forest Ant lion optimizer Multiverse optimizer grasshopper optimization algorithm

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Using Novel optimization algorithms with Support Vector Regression to Estimate Pile Settlement Rates

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INDIAN GEOTECHNICAL JOURNAL 2025年第1期55卷 79-91页

作者： Sun, Lu Li, Tinghui Huzhou Vocat & Tech Coll Sch Architecture & Engn Huzhou 313000 Zhejiang Peoples R China Huzhou Key Lab Green Bldg Technol Huzhou 313000 Zhejiang Peoples R China China Earthquake Adm Inst Engn Mech Key Lab Earthquake Engn & Engn Vibrat Harbin 150080 Heilongjiang Peoples R China

To ensure structure safety, such as bridge structures, artificial intelligence ways are the most helpful way to predict failure factors such as pile settlement estimation. Various methods are taken into account for evaluating the movement of the piles, which helps to realize the future view of the project during the loading period. The most intelligent mathematical strategy to calculate the movement of the pile is applied. In this regard, support vector regression (SVR), a machine learning method, was used in this study, accompanying two optimizers to accurately determine the key SVR variables. The marine predator algorithm (MPA) and the grasshopper optimization algorithm (GOA) were combined with SVR to create the SVR-MPA and SVR-GOA frameworks. Additionally, several metrics were used to evaluate the model's overall performance. The R2 for SVR-MPA in the training phase was found to be 0.997, showing a desirable modelling operation. At the same time, the RMSE was calculated 0.2843 mm and compared to the SVR-GOA, the differences are 1.16 and 106.38%, respectively, in favour of the former model. The comprehensive index of OBJ, including RMSE, MAE, and R2, was calculated at 0.2791 and 0.581 mm for models optimized by MPA and GOA, alternatively.

关键词： Pile Structure settlement grasshopper optimization algorithm Machine learning Support vector regression Marine predator algorithm

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Localization of Internet of Things (IoT) with Evolutionary Calculations and grasshopper optimization algorithms 6

Localization of Internet of Things (IoT) with Evolutionary C...

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6th International Conference on Signal Processing and Intelligent Systems (ICSPIS)

作者： Akbari, Mohsen Hamidzadeh, Javad Sadjad Univ Technol Fac Comp Engn & Informat Technol Mashhad Razavi Khorasan Iran

ISBN: (纸本)9781728186290

The Internet of Things has billions of nodes or smart devices. One of the main challenges of IoT is localization. Ordinarily, the Global Positioning System(GPS) is installed on all objects to localization, but this method is inefficient and expensive to install and implement, especially in cases where there are many devices. In this study, to solve this challenge, only some devices have a position finder and the rest can estimate their position with the help of hardware built into them. In this method, tags in smart objects are activated for localization. In the next step, they calculate their number of steps from smart devices with a global positioning system and estimate their distance from them. According to the specific position of devices with positioning and the estimated distance of objects without locating from them, the position of each tag is determined by the geometric method of three references. Metaheuristic methods such as grasshopper optimization algorithms are used to improve and reduce location error. The results of the experiments have been compared with state-of-the-art methods, which show the superiority of the proposed method in terms of location accuracy criteria.

关键词： grasshopper optimization algorithm Internet of Things(IoT) Localization Evolutionary calculations

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A grasshopper optimization-based fault-tolerant control algorithm for a human occupied submarine with the multi-thruster system

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OCEAN ENGINEERING 2021年 242卷 110101-110101页

作者： Zhu, Danjie Wang, Lei Hu, Zhen Yang, Simon X. Univ Guelph Sch Engn Adv Robot & Intelligent Syst ARIS Lab Guelph ON N1G 2W1 Canada China Ship Sci Res Ctr Underwater Engn Inst Wuxi 214082 Jiangsu Peoples R China

An intelligent fault-tolerant control (FTC) strategy is proposed to resolve the power loss problem for the multi thruster system fault of the manned submarine. The proposed FTC is developed on basis of the grasshopper optimization algorithm (GOA), where GOA is applied to reallocate the thruster forces when they encounter damages (power loss) of different degrees in fault cases. The reallocated forces are restricted within the physical constraints of the thruster outputs, and meanwhile are optimized to derive the torques as desired to sustain the ideal movement of the vehicle for achieving a robust controlling result. Simulations under different fault cases and comparisons to other FTCs demonstrate the effectiveness and the efficiency of the proposed GOA based FTC strategy.

关键词： Fault-tolerant control grasshopper optimization algorithm Manned submarine Multi-thruster system Power loss

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Optimum cycle length models using atom search optimization and grasshopper optimization algorithms

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第5期34卷

作者： Korkmaz, Ersin Akgungor, Ali Payidar Kirikkale Univ Dept Civil Engn Engn & Architecture Fac TR-71451 Kirikkale Turkey

A fixed-time traffic control system is widely used to manage the traffic flow at intersections. Cycle length has an important effect on the performance of the fixed-time control system and the Webster model is widely used in the literature to determine the cycle length. However, when the traffic flow ratio (Y) approaches 1, the Webster model loses its effectiveness and cannot determine the cycle length when Y is above 1. In the scope of this study, it is aimed to develop models that can predict cycle length for all traffic conditions in which are Y <= 1 and Y >= 1. Two different heuristic algorithms, atom search optimization (ASO) and grasshopper optimization algorithm (GOA) were used in the development of models and the performances of these algorithms were demonstrated. The efficiency of the developed models has been demonstrated by comparing them with both Webster's model and VISTRO optimization program. Models developed in exponential, power, and quadratic forms have been able to predict cycle lengths with lower delay values by showing better performance than Webster and VISTRO. In addition, statistical results show that the ASO approach is more successful than the GOA approach.

关键词： atom search optimization grasshopper optimization algorithm mathematical modeling optimum cycle length traffic engineering

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