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Recent swarm intelligence techniques for optimal spur gear design

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2025年

作者： Abderazek, Hammoudi Laouissi, Aissa Nouioua, Mourad Atanasovska, Ivana Mhamed Bougara Univ UR MPE Independence Ave Boumerdes 35000 Algeria Setif 1 Univ Inst Opt & Precis Mech Appl Precis Mech Lab Setif Algeria Univ Mohamed El Bachir El Ibrahimi Fac Sci & Technol Dept Mech Engn Bordj Bou Arreridj Algeria 1945 Univ 8 May Guelma Mech Engn Dept Mech & Struct Res Lab LMS Guelma Algeria King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Intelligent Mfg & Robot Dhahran Saudi Arabia Serbian Acad Arts & Sci Math Inst Belgrade Serbia

The gear tooth profile significantly influences the key operating parameters of gear pairs. By appropriately modifying the tooth profile, the vibration and noise of the gear system can be greatly reduced. In this study, four advanced optimization techniques are applied to determine the optimal geometric parameters of a cylindrical spur gear. The optimization problem is formulated with three mixed design variables: the profile shift coefficients and the normal pressure angle. The three objectives include minimizing the maximum specific sliding, sliding velocity, and nominal stresses at the pinion tooth root. Kinematic and geometric constraints, such as contact ratio, tooth thickness, and interference, are also considered to ensure an optimal spur gear design. The simulation results demonstrate that the algorithms employed are highly competitive for precision gear design optimization.

关键词： Multi-objective optimization tooth profile profile shift spur gear meta-heuristics swarm intelligence algorithms

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Predicting Academic Performance Levels in Higher Education: A Data-Driven Enhanced Fruit Fly Optimizer Kernel Extreme Learning Machine Model

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JOURNAL OF BIONIC ENGINEERING 2025年 1-23页

作者： Ye, Zhengfei Yang, Yongli Chen, Yi Chen, Huiling Wenzhou Univ Coll Fine Arts & Design Wenzhou 325000 Peoples R China Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China

Teacher-student relationships play a vital role in improving college students' academic performance and the quality of higher education. However, empirical studies with substantial data-driven insights remain limited. To address this gap, this study collected 3278 questionnaires from seven universities across four provinces in China to analyze the key factors affecting college students' academic performance. A machine learning framework, CQFOA-KELM, was developed by enhancing the Fruit Fly Optimization Algorithm (FOA) with Covariance Matrix Adaptation Evolution Strategy (CMAES) and Quadratic Approximation (QA). CQFOA significantly improved population diversity and was validated on the IEEE CEC2017 benchmark functions. The CQFOA-KELM model achieved an accuracy of 98.15% and a sensitivity of 98.53% in predicting college students' academic performance. Additionally, it effectively identified the key factors influencing academic performance through the feature selection process.

关键词： Academic achievement Machine learning Teacher-student relationships swarm intelligence algorithms Fruit fly optimization algorithm

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Modified Grey Wolf Optimizer and Application in Parameter Optimization of PI Controller

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APPLIED SCIENCES-BASEL 2025年第8期15卷 4530-4530页

作者： Sheng, Long Wu, Sen Lv, Zongyu China Univ Petr Coll Artificial Intelligence Beijing 102249 Peoples R China China Univ Petr Beijing Karamay Coll Engineer Karamay 834099 Peoples R China

The Grey Wolf Optimizer (GWO) is a well-known metaheuristic algorithm that currently has an extremely wide range of applications. However, with the increasing demand for accuracy, its shortcomings of low exploratory and population diversity are increasingly exposed. A modified Grey Wolf Optimizer (M-GWO) is proposed to tackle these weaknesses of the GWO. The M-GWO introduces mutation operators and different location-update strategies, achieving a balance between exploration and development. The experiment validated the performance of the M-GWO using the CEC2017 benchmark function and compared the results with five other advanced metaheuristic algorithms: the Improved Grey Wolf Optimizer (IGWO), GWO, Whale Optimization Algorithm (WOA), Dung Beetle Optimizer (DBO), and Harris Hawks Optimization (HHO). The performance results indicate that the M-GWO has a better performance than competitor algorithms on all 29 functions in dimensions 30 and 50, except for function 26 in dimension 30 and function 28 in dimension 50. Compared with competitor algorithms, the proposed M-GWO is the most effective algorithm, with an overall effectiveness of 96.5%. In addition, in order to show the value of the M-GWO in the practical engineering field, the M-GWO is used to optimize the PI controller parameters of the current loop of the permanent magnet synchronous motor (PMSM) system. By designing a PI controller parameter optimization scheme based on the M-GWO, the fluctuation of the q-axis current and d-axis current of the motor is reduced. The designed scheme reduces the q-axis fluctuation to around -2 similar to 1 A and the d-axis current fluctuation to around -2 similar to 2 A. By comparing the current-tracking errors of the

关键词： metaheuristic algorithms swarm intelligence algorithms grey wolf optimizer PI controller

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Wavelet-based intelligent optimization for doppler velocity estimation in the presence of celestial spectral distortion

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ADVANCES IN SPACE RESEARCH 2025年第10期75卷 7524-7539页

作者： Zhang, Zijun Liu, Jin Ning, Xiaolin Ma, Xin Wuhan Univ Sci & Technol Sch Elect Informat Wuhan 430081 Peoples R China Beihang Univ Sch Instrument Sci & Optoelect Engn Beijing 100191 Peoples R China Hefei Natl Lab Hefei 230088 Peoples R China

The variations in instrument status, along with the absorption and reflectance of planetary bodies, can cause distortions in celestial spectra that affect the accuracy of Doppler velocity estimation. High-precision Doppler velocity estimation can enhance the accuracy of the celestial velocimetry navigation. To address this issue, we propose a wavelet-based intelligent optimization for Doppler velocity estimation (WIODVE), considering that wavelet coefficients encapsulate spectral distortion signals. During the training phase, the WIODVE utilizes a weighted factor set derived from the wavelet coefficients of celestial spectra to construct the position of horned lizards, using the Doppler velocity error as the fitness function. The horned lizard optimization algorithm (HLOA) is employed to optimize the weight factor set, allowing for the reconstruction of spectral distortions. In the testing phase, the optimized weight factor set and wavelet transform are used to dynamically reconstruct the distortion of the celestial spectra. Subsequently, the reconstructed distortions are employed to correct the observed celestial spectra, with Doppler velocity estimated by the Taylor method. Additionally, we derive the Cramer-Rao lower bound (CRLB) for Doppler velocity estimation in the presence of celestial spectral distortion. Experimental results demonstrate that the WIODVE outperforms both the template enhanced radial velocity reanalysis application (TERRA) and the Taylor methods, approaching the CRLB, and exhibits strong robustness to spectral distortions in the estimation of the Doppler velocity. Furthermore, the WIODVE significantly enhances the accuracy of the celestial velocimetry navigation. (c) 2025 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： Doppler velocity estimation Doppler Shift Navigation Celestial spectra swarm intelligence algorithms Cramer-Rao bound

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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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Designing a sustainable supply chain network integrated with vehicle routing: A comparison of hybrid swarm intelligence metaheuristics

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COMPUTERS & OPERATIONS RESEARCH 2019年 110卷 220-235页

作者： Govindan, Kannan Jafarian, Ahmad Nourbakhsh, Vahid Univ Southern Denmark Ctr Sustainable Supply Chain Engn Dept Technol & Innovat Odense Denmark ATU Fac Management & Accounting Tehran Iran Univ Calif Irvine Paul Merage Sch Business Irvine CA 92697 USA

Recently, a growing concern with sustainability has become a consideration in business operations. However, there is a lack of mathematical models that quantify environmental effects and, in particular, social impacts of supply chains because of the inherently subjective nature of these aspects. To fill this gap, this paper models a distribution network in which the triple bottom lines of sustainability are captured. Different impacts of the network on the stakeholders, including company owners, workers, consumers and society, are considered as whole. In the current model, a multi-product vehicle routing problem with time windows (MPVRPTW) as an operational decision is integrated with strategic decisions related to the network design. To solve this model, three hybrid swarm intelligence techniques (particle swarm optimization (PSO), electromagnetism mechanism algorithm (EMA), and artificial bee colony (ABC)) are proposed, and each is hybridized with variable neighborhood search (VNS) are proposed. Because metaheuristic methods are sensitive to input parameters, response surface methodology (RSM) with the multi-objective decision making (MODM) approach is applied for tuning the parameters. The proposed approaches are compared with the hybrid of genetic algorithm (GA) and VNS as the benchmark algorithm. A fair comparison is conducted by employing six metrics to evaluate the quality of the Pareto frontier obtained by the algorithms on the test problems. According to the results, the predominance of EMA is enhanced by VNS local search. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Sustainable supply chain network Environmental management Corporate social responsibility Multiple-objective optimization Vehicle routing problem swarm intelligence algorithms

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A New Automatic Modulation Classifier Using swarm intelligence-trained Neural Network

A New Automatic Modulation Classifier Using Swarm Intelligen...

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2nd International Conference on Energy and Environmental Protection (ICEEP 2013)

作者： Cheng, Li Liu, Jin Harbin Engn Univ Coll Informat & Commun Engn Harbin Peoples R China Inst China Ship Ind Corp Wuhan Peoples R China

ISBN: (纸本)9783037857441

Monitoring modulation type of the detected signal is the most important intermediate step between signal detection and demodulation. The back propagation neural network (BPNN) was widely used in constructing modulated signal classifier in the field of automatic modulation classification (AMC). There are many visible features in the back propagation (BP) algorithm including adaptive learning, the ability of fault tolerant, etc. However, this algorithm has two main disadvantages, such as the slow convergence speed and easily falling into the local minimum. This paper presents a novel modulation classifier using BPNN trained with swarm intelligence algorithms (SIA), for the sake of overcoming these deficiencies. The initial weights and thresholds of BP neural network were optimized by SIA. As the SIA has an excellent global search property, this classifier can consume less training time and improve the automatic modulation type identification rate.

关键词： automatic modulation classifier back propagation neural network swarm intelligence algorithms

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Comparison of swarm intelligence Methods for Suppressing PAPR in CO-OFDM Systems 15

Comparison of Swarm Intelligence Methods for Suppressing PAP...

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15th International Conference on Optical Communications and Networks (ICOCN)

作者： Wang, Shaoying Liu, Jianfei Yi, Ziyao Zeng, Xiangye Lu, Jia Wang, Mengjun Hebei Univ Technol Sch Elect & Informat Engn Key Lab Elect Mat & Devices Tianjin Tianjin Peoples R China

ISBN: (纸本)9781509034918

swarm intelligence algorithms are applied to suppressing peak to average power ratio (PAPR) in CO-OFDM system by optimizing phase of subcarriers. The result shows that all of these algorithms can effectively reduce PAPR, and the PAPR of CSO optimized signal is reduced by 1.5dB, 0.77dB and 0.28dB compared with that of PSO, BSA and BA optimized signals.

关键词： PAPR CO-OFDM Number of population swarm intelligence algorithms

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Improved fuzzy clustering with swarm intelligence for medical image analysis 6

Improved fuzzy clustering with swarm intelligence for medica...

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

作者： Gholami, Fateme Sadjad Univ Technol Fac Comp Engn & Informat Technol Mashhad Razavi Khorasan Iran

ISBN: (纸本)9781728186290

One of the challenges in the world today is the existence of a variety of diseases, some of which require the processing of medical images to diagnose and evaluate, such as images of brain tumors. One of the methods of analyzing and evaluating patients related to the brain is magnetic resonance imaging(MRI). Data mining methods such as clustering can be used to analyze magnetic resonance images. Clustering techniques can take the area of brain tumors from brain tissue and use it to diagnose disease. Various clustering methods have been proposed so far, one of which is the fuzzy clustering or FCM method, and it has a high accuracy for clustering and segmentation of brain tissues. Fuzzy clustering is less sensitive to the noise in these images and therefore its segmentation accuracy is somewhat desirable. To improve the performance of FCM clustering, in identifying the edges and borders of tumors, it is necessary to select the optimal clustering centers. The optimal selection of cluster centers increases its accuracy in learning and segmentation. Given that the optimal selection of cluster centers is an optimization method, metaheuristic algorithms can be used for this purpose. In this research, swarm intelligence algorithms have been used to optimally select cluster centers in FCM. The analysis of the proposed method on a set of images of brain magnetic resonance shows that the proposed algorithm has the specificity, sensitivity, and accuracy of 96.87%, 88.36%, and 91.32% in the diagnosis of brain tumors, respectively. The proposed method of hybrid methods, such as the fuzzy method, better detects brain tumors.

关键词： swarm intelligence algorithms image processing magnetic resonance brain brain tumor

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Using swarm intelligence to Generate Test Data for Covering Prime Paths 1

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7th IPM International Conference on Fundamentals of Software Engineering (FSEN)

作者： Bidgoli, Atieh Monemi Haghighi, Hassan Nasab, Tahere Zohdi Sabouri, Hamideh Shahid Beheshti Univ Dept Comp Sci & Engn GC Tehran Iran

ISBN: (数字)9783319689722

ISBN: (纸本)9783319689722;9783319689715

Search-based test data generation methods mostly consider the branch coverage criterion. To the best of our knowledge, only two works exist which propose a fitness function that can support the prime path coverage criterion, while this criterion subsumes the branch coverage criterion. These works are based on the Genetic Algorithm (GA) while scalability of the evolutionary algorithms like GA is questionable. Since there is a general agreement that evolutionary algorithms are inferior to swarm intelligence algorithms, we propose a new approach based on swarm intelligence for covering prime paths. We utilize two prominent swarm intelligence algorithms, i.e., ACO and PSO, along with a new normalized fitness function to provide a better approach for covering prime paths. To make ACO applicable for the test data generation problem, we provide a customization of this algorithm. The experimental results show that PSO and the proposed customization of ACO are both more efficient and more effective than GA when generating test data to cover prime paths. Also, the customized ACO, in comparison to PSO, has better effectiveness while has a worse efficiency.

关键词： Search based test data generation Prime paths swarm intelligence algorithms Ant colony optimization Particle swarm optimization

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