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Multi-trial Vector-based Whale Optimization Algorithm

Journal of Bionic Engineering 2024年第3期21卷 1465-1495页

作者： Mohammad H.Nadimi-Shahraki Hajar Farhanginasab Shokooh Taghian Ali Safaa Sadiq Seyedali Mirjalili Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad***Iran Big Data Research Center Najafabad BranchIslamic Azad UniversityNajafabad***Iran Department of Computer Science Nottingham Trent UniversityNottinghamNG118NSUK Centre for Artificial Intelligence Research and Optimisation Torrens UniversityBrisbane4006Australia

The Whale Optimization Algorithm(WOA)is a swarm intelligence metaheuristic inspired by the bubble-net hunting tactic of humpback *** spite of its popularity due to simplicity,ease of implementation,and a limited number of parameters,WOA’s search strategy can adversely affect the convergence and equilibrium between exploration and exploitation in complex *** address this limitation,we propose a new algorithm called Multi-trial Vector-based Whale Optimization Algorithm(MTV-WOA)that incorporates a Balancing Strategy-based Trial-vector Producer(BS_TVP),a Local Strategy-based Trial-vector Producer(LS_TVP),and a Global Strategy-based Trial-vector Producer(GS_TVP)to address real-world optimization problems of varied degrees of ***-WOA has the potential to enhance exploitation and exploration,reduce the probability of being stranded in local optima,and preserve the equilibrium between exploration and *** the purpose of evaluating the proposed algorithm's performance,it is compared to eight metaheuristic algorithms utilizing CEC 2018 test ***,MTV-WOA is compared with well-stablished,recent,and WOA variant *** experimental results demonstrate that MTV-WOA surpasses comparative algorithms in terms of the accuracy of the solutions and convergence ***,we conducted the Friedman test to assess the gained results statistically and observed that MTV-WOA significantly outperforms comparative ***,we solved five engineering design problems to demonstrate the practicality of *** results indicate that the proposed MTV-WOA can efficiently address the complexities of engineering challenges and provide superior solutions that are superior to those of other algorithms.

关键词： swarm intelligence algorithms Metaheuristic algorithms Optimization Engineering design problems Whale optimization algorithm

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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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Cost-Minimized Computation Offloading and User Association in Hybrid Cloud and Edge Computing

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IEEE INTERNET OF THINGS JOURNAL 2024年第9期11卷 16672-16683页

作者： Bi, Jing Wang, Ziqi Yuan, Haitao Zhang, Jia Zhou, Mengchu Beijing Univ Technol Fac Informat Technol Sch Software Engn Beijing 100124 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Southern Methodist Univ Lyle Sch Engn Dept Comp Sci Dallas TX 75205 USA New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

Smart mobile devices (SMDs) are integral for running advanced applications that demand significant computing resources and quick response time, e.g., immersive gaming and advanced image editing. However, SMDs often face constraints in computational capacity and battery duration, restricting their ability to process these tasks instantaneously. Cloud computing can circumvent these limitations by computation offloading, but cloud data centers (CDCs) are often deployed at long distances from users, which results in longer computational latency. To address the latency issue, the incorporation of small base stations (SBSs) in the vicinity of the user provides services with high bandwidth and low latency. The primary challenge lies in balancing the economics of the system consisting of different SMDs, SBSs, and a CDC, i.e., minimizing cost while still meeting the latency requirements of applications. In this work, a cost-minimized computation offloading framework is formulated and solved by a two-stage optimization algorithm named L & eacute;vy flight and simulated annealing-based grey wolf optimizer (LSAG). The optimal edge selection strategy is defined in the first stage for dealing with the case of several available SBSs. The second stage coordinates task scheduling and optimizes the allocation of resources among SMDs, SBSs, and CDC. LSAG integrates the extended search property of L & eacute;vy flight and the individual selection strategy of simulated annealing in the grey wolf optimizer, which reduces the risk of falling into local optima and finds the global optimum. Experimental results of executing real-life tasks show that LSAG outperforms its state-of-the-art peers in terms of cost and speed of convergence.

关键词： Costs Task analysis Resource management Optimization Energy consumption Servers Computer architecture Cloud computing computation offloading edge computing grey wolf optimizer (GWO) swarm intelligence algorithms

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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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Review of the grey wolf optimization algorithm: variants and applications

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NEURAL COMPUTING & APPLICATIONS 2024年第6期36卷 2713-2735页

作者： Liu, Yunyun As'arry, Azizan Hassan, Mohd Khair Hairuddin, Abdul Aziz Mohamad, Hesham Chongqing Coll Elect Engn Sch Intelligent Mfg & Automobile Chongqing Peoples R China Univ Putra Malaysia Fac Engn Seri Kembangan Malaysia

One of the most widely referenced swarm intelligence (SI) algorithms is the Grey Wolf Optimizer (GWO), which is based on the pack hunting and natural leadership organization of grey wolves. The GWO algorithm offers several significant benefits, including simple implementation, rapid convergence, and superior convergence outcomes, leading to its effective application in diverse fields for solving optimization issues. Consequently, the GWO has rapidly garnered substantial research interest and a broad audience across numerous areas. To better understand the literature on this algorithm, this review paper aims to consolidate and summarize research publications that utilized the GWO. The paper begins with a concise introduction to the GWO, providing insight into its natural establishment and conceptual framework for optimization. It then lays out the theoretical foundation and key procedures involved in the GWO, following which it comprehensively examines the most recent iterations of the algorithm and categorizes them into parallel, modified, and hybridized variations. Subsequently, the primary applications of the GWO are thoroughly explored, spanning various fields such as computer science, engineering, energy, physics and astronomy, materials science, environmental science, and chemical engineering, among others. This review paper concludes by summarizing the key arguments in favour of GWO and outlining potential lines of inquiry in the future research.

关键词： GWO swarm intelligence algorithms Variants Applications

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Discrete Improved Grey Wolf Optimizer for Community Detection

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Journal of Bionic Engineering 2023年第5期20卷 2331-2358页

作者： Mohammad H.Nadimi-Shahraki Ebrahim Moeini Shokooh Taghian Seyedali Mirjalili Faculty of Computer Engineering Najafabad BranchIslamic Azad UniversityNajafabad***Iran Big Data Research Center Najafabad BranchIslamic Azad UniversityNajafabad***Iran Centre for Artificial Intelligence Research and Optimisation Torrens UniversityBrisbane4006Australia Yonsei Frontier Lab Yonsei UniversitySeoulSouth Korea

Detecting communities in real and complex networks is a highly contested topic in network *** many metaheuristic-based algorithms for community detection have been proposed,they still cannot effectively fulfill large-scale and real-world ***,this paper presents a new discrete version of the Improved Grey Wolf Optimizer(I-GWO)algorithm named DI-GWOCD for effectively detecting communities of different *** the proposed DI-GWOCD algorithm,I-GWO is first armed using a local search strategy to discover and improve nodes placed in improper communities and increase its ability to search for a better *** a novel Binary Distance Vector(BDV)is introduced to calculate the wolves’distances and adapt I-GWO for solving the discrete community detection *** performance of the proposed DI-GWOCD was evaluated in terms of modularity,NMI,and the number of detected communities conducted by some well-known real-world network *** experimental results were compared with the state-of-the-art algorithms and statistically analyzed using the Friedman and Wilcoxon *** comparison and the statistical analysis show that the proposed DI-GWOCD can detect the communities with higher quality than other comparative algorithms.

关键词： Community detection Complex network Optimization Metaheuristic algorithms swarm intelligence algorithms Grey wolf optimizer algorithm

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Leveraging swarm intelligence for Invariant Rule Generation and Anomaly Detection in Industrial Control Systems

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APPLIED SCIENCES-BASEL 2024年第22期14卷 10705页

作者： Song, Yunkai Huang, Huihui Wang, Hongmin Wei, Qiang Informat Engn Univ Sch Cyberspace Secur Zhengzhou 450007 Peoples R China

Industrial control systems (ICSs), which are fundamental to the operation of critical infrastructure, face increasingly sophisticated security threats due to the integration of information and operational technologies. Conventional anomaly detection techniques often lack the ability to provide clear explanations for their detection, and their inherent complexity can impede practical implementation in the resource-constrained environments typical of ICSs. To address these challenges, this paper proposes a novel approach that leverages swarm intelligence algorithms for the extraction of numerical association rules, specifically designed for anomaly detection in ICS. The proposed approach is designed to effectively identify and precisely localize anomalies by analyzing the states of sensors and actuators. Experimental validation using the Secure Water Treatment (SWaT) dataset demonstrates that the proposed approach can detect over 84% of attack instances, with precise anomaly localization achievable by examining as few as two to six sensor or actuator states. This significantly improves the efficiency and accuracy of anomaly detection. Furthermore, since the method is based on the general control dynamics of ICSs, it demonstrates robust generalization, making it applicable across a wide range of industrial control systems.

关键词： industrial control systems anomaly detection numerical association rules swarm intelligence algorithms security enhancement

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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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Forecasting the trend of tuberculosis incidence in Anhui Province based on machine learning optimization algorithm, 2013-2023

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BMC PULMONARY MEDICINE 2024年第1期24卷 536页

作者： Zhang, Yan Ma, Huan Wang, Hua Xia, Qing Wu, Shasha Meng, Jing Zhu, Panpan Guo, Zhilong Hou, Jing Anhui Chest Hosp Dept TB 3 397 Jixi Rd Hefei 230000 Peoples R China 901th Hosp Joint Logist Support Force PLA Dept Oncol Hefei 230032 Peoples R China Anhui Chest Hosp Dept TB 8 Hefei Peoples R China

Tuberculosis has been one of the most common communicable diseases raising global concerns. Accurately predicting the incidence of Tuberculosis remains challenging. Here we constructed a time-series analysis and fusion tool using multi-source data, and aimed to more accurately predict the incidence trend of tuberculosis of Anhui Province from 2013 to 2023. Random forest algorithm (RF), Feature Recursive Elimination (RFE) and Least absolute shrinkage and selection operator (LASSO) were implemented to improve the derivation of features related to infectious diseases and feature work. Based on the characteristics of infectious disease data, a model of RF-RFE-LASSO integrated particle swarm optimization multiple inputs long short term memory recurrent neural network (RRL-PSO-MiLSTM) was created to perform more accurate prediction. Results showed that the PSO-MiLSTM achieved excellent prediction results compared with common single-input and multi-input time-series models (test set MSE:42.3555, MAE: 59.3333, RMSE: 146.7237, MAPE: 2.1133, R2: 0.8634). PSO-MiLSTM enriches and complements the methodological research content of calibrating the time-series predictive analysis of infectious diseases using multi-source data, and can be used as a brand-new benchmark for the analysis of influencing factors and trend prediction of infectious diseases at the public health level in the future, as well as providing a reference for incidence rate prediction of infectious diseases.

关键词： Time-series analysis Tuberculosis Feature engineering Neural network swarm intelligence algorithms

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