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一种基于VMD-SABO-XGBoost的隐伏断层密集度预测模型及分区评价方法 认领 引用
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作者 华明国 周爱桃 +4 位作者 马帅 王一达 李延续 李康 李梦瑶 《煤矿安全》 CAS 北大核心 2026年第4期144-155,共12页
隐伏断层是煤矿生产中的重要地质问题,隐伏断层的规模往往较小,难以进行准确预测。为研究适用于隐伏断层预测的机器学习优化算法,提出了一种基于VMD-SABO-XGBoost的隐伏断层密集度预测模型和断层复杂程度分区评价方法。利用变分模态分解... 隐伏断层是煤矿生产中的重要地质问题,隐伏断层的规模往往较小,难以进行准确预测。为研究适用于隐伏断层预测的机器学习优化算法,提出了一种基于VMD-SABO-XGBoost的隐伏断层密集度预测模型和断层复杂程度分区评价方法。利用变分模态分解(VMD)算法将矿井现场搜集的瓦斯含量、坚固性系数等初始数据分解为多个模态分量,对其进行特征提取,并通过灰色关联分析法计算预测特征的关联度排名,其中断层强度参数、断层分维值、瓦斯含量、煤层底板倾角变异系数、坚固性系数5项特征指标与断层赋存关联度最大;在SABO寻优算法中引入精英反向学习和自适应权重因子,解决模型过早陷入“局部最优解”、收敛性能较差以及特征种群多样性不足的问题,通过实验验证优化前后算法的准确率差异,证明优化后的SABO极大提高了收敛效率,模型预测准确度升高;结合XGBoost机器学习算法组成隐伏断层预测模型。以山西长治某矿井3#煤掘进工作面断层为实验对象,建立断层类型综合分区评价等级,并作为预测标签与传统使用的XGBoost模型、VMD-XGBoost模型和SABO-XGBoost模型进行对比,结果显示,VMD-SABO-XGBoost模型预测精度最高。经过实例验证,和现场实际断层复杂程度对应,验证了该方法的准确性和适应性,可以应用于预测矿井隐伏断层密集度和复杂程度。 展开更多
关键词 隐伏断层 密集度预测 变分模态分解 SABO算法优化 XGBoost
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HERO(Hessian-Engineered Relaxation Optimizer):Suppressing“Hessian Pollution”for Accelerated First-Principles Structural Relaxation 认领 引用
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作者 Mingzhe Li Piao Ma +2 位作者 Limin Li Weijie Yang Hao Li 《Computers, Materials & Continua》 SCIE EI 2026年第7期266-277,共12页
Structural optimization is a fundamental step in density functional theory(DFT)calculations,typically driven by the Broyden-Fletcher-Goldfarb-Shanno(BFGS)optimizer.However,the standard BFGS algorithm relies on a local... Structural optimization is a fundamental step in density functional theory(DFT)calculations,typically driven by the Broyden-Fletcher-Goldfarb-Shanno(BFGS)optimizer.However,the standard BFGS algorithm relies on a local quadratic approximation of the potential energy surface(PES),which frequently breaks down in highly non-quadratic regimes typical of complex surface adsorption systems and defective bulk materials.This breakdown leads to“Hessian pollution”,a phenomenon where higher-order anharmonicities introduce spurious off-diagonal inter-atomic couplings that distort curvature estimates and significantly stall convergence.Herein,we propose a physics-inspired algorithmic intervention to the BFGS method that systematically suppresses this pollution.Once the maximum residual force drops below a specific activation threshold(e.g.,0.5 or 0.1 eV/Å),our approach conditionally resets all off-diagonal Hessian blocks,and introduces an isotropic background stiffness strategy where these blocks can be repopulated with a small positive constant rather than zeroed completely.This balances the robust stability of diagonal dominance with accelerated convergence speed.Implemented as an add-on to the Atomic Simulation Environment(ASE)Library,the method is lightweight,transferable,and compatible with standard DFT codes.Tests across diverse chemical systems,including atomic and molecular adsorbates(O*,H*,CO*)on Pt(111)surfaces and defective bulk oxides(WO3-x),demonstrate substantial reductions in the number of required force calls without biasing the final optimized geometry.It offers a practical tool for high-throughput DFT workflows that eliminates the need for domain-specific training.This method is available via our open-source package,Hessian-Engineered Relaxation Optimizer(HERO). 展开更多
关键词 Hessian-engineered relaxation optimizer density functional theory(DFT) structural optimization BFGS algorithm hessian pollution structural relaxation
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Several Improved Models of the Mountain Gazelle Optimizer for Solving Optimization Problems 认领 引用
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作者 Farhad Soleimanian Gharehchopogh Keyvan Fattahi Rishakan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期727-780,共54页
Optimization algorithms are crucial for solving NP-hard problems in engineering and computational sciences.Metaheuristic algorithms,in particular,have proven highly effective in complex optimization scenarios characte... Optimization algorithms are crucial for solving NP-hard problems in engineering and computational sciences.Metaheuristic algorithms,in particular,have proven highly effective in complex optimization scenarios characterized by high dimensionality and intricate variable relationships.The Mountain Gazelle Optimizer(MGO)is notably effective but struggles to balance local search refinement and global space exploration,often leading to premature convergence and entrapment in local optima.This paper presents the Improved MGO(IMGO),which integrates three synergistic enhancements:dynamic chaos mapping using piecewise chaotic sequences to boost explo-ration diversity;Opposition-Based Learning(OBL)with adaptive,diversity-driven activation to speed up convergence;and structural refinements to the position update mechanisms to enhance exploitation.The IMGO underwent a comprehensive evaluation using 52 standardised benchmark functions and seven engineering optimization problems.Benchmark evaluations showed that IMGO achieved the highest rank in best solution quality for 31 functions,the highest rank in mean performance for 18 functions,and the highest rank in worst-case performance for 14 functions among 11 competing algorithms.Statistical validation using Wilcoxon signed-rank tests confirmed that IMGO outperformed individual competitors across 16 to 50 functions,depending on the algorithm.At the same time,Friedman ranking analysis placed IMGO with an average rank of 4.15,compared to the baseline MGO’s 4.38,establishing the best overall performance.The evaluation of engineering problems revealed consistent improvements,including an optimal cost of 1.6896 for the welded beam design vs.MGO’s 1.7249,a minimum cost of 5885.33 for the pressure vessel design vs.MGO’s 6300,and a minimum weight of 2964.52 kg for the speed reducer design vs.MGO’s 2990.00 kg.Ablation studies identified OBL as the strongest individual contributor,whereas complete integration achieved superior performance through synergistic interactions among components.Computational complexity analysis established an O(T×N×5×f(P))time complexity,representing a 1.25×increase in fitness evaluation relative to the baseline MGO,validating the favorable accuracy-efficiency trade-offs for practical optimization applications. 展开更多
关键词 Metaheuristic algorithm dynamical chaos integration opposition-based learning mountain gazelle optimizer optimization
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基于GS-SABO-BPNN和MOPSOCD的激光熔覆工艺参数优化方法 认领 引用 被引量：4
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作者 彭云川 杜彦斌 +2 位作者 毛恺奕 陈泓西 涂坚 《计算机集成制造系统》 EI CSCD 北大核心 2026年第4期1238-1251,共14页
为了揭示工艺参数对涂层质量特征的影响,获得制备高质量涂层的最优工艺参数组合,提出一种基于GS-SABO-BPNN和MOPSOCD的激光熔覆工艺参数优化方法。以2507不锈钢表面激光熔覆制备Stellite6涂层为例,设计L25(53)的正交实验,开展激光熔覆实... 为了揭示工艺参数对涂层质量特征的影响,获得制备高质量涂层的最优工艺参数组合,提出一种基于GS-SABO-BPNN和MOPSOCD的激光熔覆工艺参数优化方法。以2507不锈钢表面激光熔覆制备Stellite6涂层为例,设计L25(53)的正交实验,开展激光熔覆实验;分析了激光熔覆工艺参数对宽高比、稀释率和显微硬度的影响规律,并基于GS-SABO-BPNN建立工艺参数与质量特征之间的映射模型;采用MOPSOCD算法对工艺参数进行优化,获得Pareto解集,并采用EWM-TOPSIS综合评价决策方法对Pareto解集进行排序,获得最优工艺参数组合。结果表明,相较于工程经验,优化后的涂层宽高比和稀释率更接近理论最优值,显微组织更细密,显微硬度更高,所制备涂层的形貌和性能更优异。本研究提出的激光熔覆工艺参数优化方法可以显著改善单道熔覆层的质量特征。 展开更多
关键词 激光熔覆 参数优化 GS-SABO-BPNN MOPSOCD 综合评价
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Remaining useful life prediction for bearings based on Transformer-BiLSTM network optimized by the Newton-Raphson-based optimizer 认领 引用
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作者 Wen Juan Wu You +1 位作者 Song Yang Pan Baisong 《High Technology Letters》 EI CAS 2026年第2期109-120,共12页
The rolling bearing is one of the critical components in mechanical equipment,and predicting its remaining useful life(RUL)is of great significance in enterprise production processes.While deep learning-based approach... The rolling bearing is one of the critical components in mechanical equipment,and predicting its remaining useful life(RUL)is of great significance in enterprise production processes.While deep learning-based approaches have achieved great success for bearing prognosis,most of them are not capable of mining both global and local information from the run-to-failure data.In addition,hyperparameters such as the number of hidden layer neurons,learning rate,and regularization parameters in neural networks still rely heavily on manual experience for setting.To address these issues,a novel framework for predicting the RUL of bearings based on the Transformer and the bidirectional long short-term memory(Transformer-BiLSTM)is proposed,and the Newton-Raphsonbased optimizer(NRBO)is introduced to determine the crucial parameters of the network.Firstly,degradation sensitive features are extracted and selected from the raw vibration signals,forming the input for the prediction model.Secondly,the mean absolute error(MAE)between the predicted and actual values is utilized as the fitness function of the NRBO algorithm to optimize the Transformer-BiLSTM model,searching for the optimal values of the key hyperparameters.Finally,the optimized model is used for RUL prediction,and its performance is validated on publicly available datasets.The results demonstrate that the proposed method can achieve the optimal hyperparameter combination without relying on empirical guidance.Compared with the unoptimized model,the optimized prediction model reduces the MAE and root mean squared error(RMSE)by 6.50%and 9.91%,respectively. 展开更多
关键词 Transformer bidirectional long short-term memory Newton-Raphson-based optimizer remaining useful life prediction bearing
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A Modified Gorilla Troops Optimizer-Based Explainable Machine Learning for Early Cardiovascular Disease Prediction 认领 引用
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作者 Israt Jahan Afsana Begum +5 位作者 Bibhas Roy Chowdhury Piyas Fahmid Al Farid Fatama Jannat Tisha Shahrin Islam Abu Saleh Musa Miah Hezerul Abdul Karim 《Computers, Materials & Continua》 SCIE EI 2026年第9期665-690,共26页
Transforming underlying cardiovascular risk into actionable clinical decisions remains a major challenge in contemporary healthcare.Despite advances in cardiology,early-stage cardiovascular disease often remains undet... Transforming underlying cardiovascular risk into actionable clinical decisions remains a major challenge in contemporary healthcare.Despite advances in cardiology,early-stage cardiovascular disease often remains undetected,which hinders timely intervention and leads to preventable deaths.To overcome this problem,this study presents an explainable machine learning framework for the early diagnosis of cardiovascular disease(CVD).Initially,this study examined several data-balancing strategies,for example,SMOTE(Synthetic Minority Oversampling Technique),SMOTETomek(Synthetic Minority Over-sampling Technique+Tomek Links),Tomek Links,ADASYN(Adaptive Synthetic Sampling),and SMOTE-ENN(Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors)within the data-preprocessing pipeline.We proposed a novel Adaptive Inertia Weight Gorilla Troops Optimizer(AIW-GTO)to overcome classical GTO’s(Gorilla Troops Optimizer)unstable convergence by adaptively controlling step sizes.It uses large exploratory steps early for wide search and smaller steps later for finetuned local optimization,which ensures stable convergence and enhanced optimization accuracy.Several machine learning techniques,namely XGBoost,Random Forest,SVM(Support Vector Machine),LightGBM(Light Gradient Boosting Machine),and MLP(Multilayer Perceptron)classifier,were evaluated on the multi-regional UCI heart disease dataset.The experimental findings revealed that,by integrating AIW-GTO Optimization and class imbalance mitigation,LightGBM and XGBoost individually achieved a benchmark accuracy of 93.48%and 91.85%,respectively.Moreover,a weighted ensemble of them further improved the accuracy to 94.02%.Sensitivity analysis further evaluated the model’s ability to perform under incomplete clinical test data.To enhance ethical considerations and clinical trust,SHAP(SHapley Additive exPlanations)and LIME(Local Interpretable Model-agnostic Explanations)were utilized to provide model explainability and identify the most influential features affecting prediction outcomes.Analysis indicated that ECG-related(Electrocardiogram)features,including ST_Slope(exercise-induced ST change)and Oldpeak(ST depression magnitude),emerged as key predictors of CVD risk.Overall,the proposed framework provides a clinically reliable and interpretable approach for early cardiovascular risk assessment to enable proactive patient management. 展开更多
关键词 Cardiovascular disease(CVD) machine learning preventive cardiology class imbalance handling model optimization gorilla troops optimizer UCI heart disease dataset explainable AI(XAI) SHAP LIME ensemble technique automated diagnosis clinical decision support system(CDSS) risk stratification
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A Stochastic Multi-Objective Framework for Wind DG Allocation and Dynamic Reconfiguration:Minimizing Losses and Enhancing Reliability with an Improved Grey Wolf Optimizer 认领 引用
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作者 Ali S.Alghamdi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期704-744,共41页
The integration of wind-based DG introduces significant variability and uncertainty into the operation of distribution networks,which complicates the planning and decision-making process.This paper presents a dualobje... The integration of wind-based DG introduces significant variability and uncertainty into the operation of distribution networks,which complicates the planning and decision-making process.This paper presents a dualobjective stochastic optimization framework for the optimal allocation of wind DG,considering dynamic network reconfiguration across multiple loading conditions.Probabilistic modeling of wind speed is integrated using the Weibull distribution and the associated wind power uncertainty is discretized through a scenario-based point estimation method.Variability in load is accounted for by considering multiple loading levels,and the integrated uncertainty space is constructed as the Cartesian product of wind scenarios and load profiles.The optimization seeks to minimize the total energy losses together with the enhancement of reliability,quantified through the expected energy not supplied.For the solution of the complex,nonlinear,multi-objective problem,the Improved Multi-Objective Grey Wolf Optimizer(I-MGWO)is developed,including quasi-oppositional population seeding,adaptive stochastic coeficient strategy,and dynamic convex combination position update.Simulation results on the IEEE 33-bus system demonstrate that the proposed integrated strategy of simultaneous wind DG allocation and network reconfiguration gives synergistic improvements,yielding up to 55.7%reduction in energy losses,and a reduction of up to 61.4%in EENS over the base case.In both convergence speed and solution quality,I-MGWO consistently outperforms conventional algorithms and gives a robust and computationally efficient tool for distribution system planning under uncertainty. 展开更多
关键词 Wind power generation distributed generation allocation active distribution network network reconfiguration uncertainty modeling stochastic optimization multi-objective optimization reliability assessment Grey Wolf Optimizer
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引水工程边坡位移SABO-LSTM监测模型及风险状态研究 认领 引用
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作者 黄铭 王鹏飞 《合肥工业大学学报（自然科学版）》 CAS 北大核心 2026年第7期997-1002,共6页
文章利用减法平均优化算法(subtraction-average-based optimizer,SABO)对长短期记忆网络(long shortterm memory,LSTM)模型进行优化,建立SABO-LSTM引水工程边坡位移监测模型,实现了对边坡位移规律的准确预测。并以边坡各测点位移和位... 文章利用减法平均优化算法(subtraction-average-based optimizer,SABO)对长短期记忆网络(long shortterm memory,LSTM)模型进行优化,建立SABO-LSTM引水工程边坡位移监测模型,实现了对边坡位移规律的准确预测。并以边坡各测点位移和位移速率为评估指标,根据各指标数据特征采用置信区间法对其进行风险状态分级,基于区间数距离计算得到各指标的基本概率分配;然后结合D-S(Dempster-Shafer)证据理论对各指标基本概率进行融合,利用融合结果对边坡位移风险状态进行评估,并结合监测模型预测结果,实现了对边坡位移风险状态的预评估。该研究结果可为边坡位移预测及风险状态评估提供技术参考。 展开更多
关键词 引水工程边坡 SABO-LSTM模型 置信区间法 D-S证据理论 风险状态
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基于SABO-VMD与改进KELM的水电机组故障诊断 认领 引用 被引量：3
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作者 张彬桥 高志伟 +1 位作者 陈庆松 章泽生 《人民长江》 北大核心 2026年第6期252-259,276,共8页
为了提高水电机组故障诊断的精度,提出一种由减法平均优化算法(SABO)优化变分模态分解(VMD)及改进蜣螂优化算法(IDBO)-核极限学习机(KELM)联合构建的水电机组故障诊断模型。首先,采用SABO算法来优化VMD的重要参数(惩罚因子α和分解个数... 为了提高水电机组故障诊断的精度,提出一种由减法平均优化算法(SABO)优化变分模态分解(VMD)及改进蜣螂优化算法(IDBO)-核极限学习机(KELM)联合构建的水电机组故障诊断模型。首先,采用SABO算法来优化VMD的重要参数(惩罚因子α和分解个数K);提取SABO-VMD分解排列熵与互信息熵的复合函数最小本征模态分量(IMF)作为最优分量,计算其相关时域特征参数并构建故障信号特征向量;然后引入Tent混沌映射和自适应t分布扰动多种策略对蜣螂优化算法进行改进,并利用IDBO算法对KELM模型进行参数优化,构建IDBO-KELM水电机组故障诊断模型;最后采用转子实验平台模拟机组轴系故障,对模型进行验证。验证结果表明:该方法在水电机组轴系故障诊断方面的准确率达到99.375%。研究成果可为高精度水电机组故障诊断提供思路和方案。 展开更多
关键词 水电机组故障诊断 减法平均优化算法 模态分解 改进蜣螂优化算法 核极限学习机
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Concrete Strength Prediction Using Machine Learning and Somersaulting Spider Optimizer 认领 引用 被引量：1
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作者 Marwa M.Eid Amel Ali Alhussan +2 位作者 Ebrahim A.Mattar Nima Khodadadi El-Sayed M.El-Kenawy 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期465-493,共29页
Accurate prediction of concrete compressive strength is fundamental for optimizing mix designs,improving material utilization,and ensuring structural safety in modern construction.Traditional empirical methods often f... Accurate prediction of concrete compressive strength is fundamental for optimizing mix designs,improving material utilization,and ensuring structural safety in modern construction.Traditional empirical methods often fail to capture the non-linear relationships among concrete constituents,especially with the growing use of supple-mentary cementitious materials and recycled aggregates.This study presents an integrated machine learning framework for concrete strength prediction,combining advanced regression models—namely CatBoost—with metaheuristic optimization algorithms,with a particular focus on the Somersaulting Spider Optimizer(SSO).A comprehensive dataset encompassing diverse mix proportions and material types was used to evaluate baseline machine learning models,including CatBoost,XGBoost,ExtraTrees,and RandomForest.Among these,CatBoost demonstrated superior accuracy across multiple performance metrics.To further enhance predictive capability,several bio-inspired optimizers were employed for hyperparameter tuning.The SSO-CatBoost hybrid achieved the lowest mean squared error and highest correlation coefficients,outperforming other metaheuristic approaches such as Genetic Algorithm,Particle Swarm Optimization,and Grey Wolf Optimizer.Statistical significance was established through Analysis of Variance and Wilcoxon signed-rank testing,confirming the robustness of the optimized models.The proposed methodology not only delivers improved predictive performance but also offers a transparent framework for mix design optimization,supporting data-driven decision making in sustainable and resilient infrastructure development. 展开更多
关键词 Concrete strength machine learning CatBoost metaheuristic optimization somersaulting spider optimizer ensemble models
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Multistrategy Improved Aquila Optimizer for Test Case Prioritization 认领 引用
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作者 Jiali Chen Jiheng Zhang +3 位作者 Xiaojie Chen Chong Zeng Honghui Yi Heming Jia 《Computers, Materials & Continua》 SCIE EI 2026年第8期2328-2362,共35页
Traditional heuristic algorithms often fall into local optima and converge slowly when test case prioritization is addressed in regression testing,making them inadequate for complex real-world scenarios.The Aquila opt... Traditional heuristic algorithms often fall into local optima and converge slowly when test case prioritization is addressed in regression testing,making them inadequate for complex real-world scenarios.The Aquila optimizer,a novel metaheuristic algorithm,demonstrates strong global exploration capability but still faces limitations,including insufficient exploitation capability and slow convergence.To overcome these challenges,a multi-strategy improved chaotic Cauchy inverse cumulative distribution Aquila optimizer for test case prioritization is proposed.First,a logistic–sine–cosine composite chaotic mapping is introduced during the initialization phase of the Aquila optimizer to increase population diversity.Second,the mutated random walk strategy is used to improve global exploration,further enhancing the global search ability of the Aquila optimizer.Moreover,during the narrowed exploration and narrowed exploitation phases,the Cauchy inverse cumulative distribution flight replaces the Lévy flight strategy to reallocate individual positions,strengthening individuals’optimization capability and preventing the algorithm from becoming trapped in local optima.Finally,in the later iteration stage,the specular reflection learning strategy is used to perturb the optimal individual positions and improve the Aquila optimizer’s convergence accuracy and comprehensive optimization performance.Five Java projects were selected from the Defects4J benchmark datasets to conduct comparative experiments with the Aquila optimizer and seven other metaheuristic algorithms.The results demonstrate the effectiveness and superiority of the improved algorithm in test case prioritization.It achieves average improvements of approximately 4.96%in the average percentage of fault detection,3.82%in the average percentage of block coverage,and 5.64%in the average percentage of decision coverage,enabling faster coverage of code blocks and branches.The results provide an efficient priority sorting solution for complex regression testing scenarios. 展开更多
关键词 Heuristic algorithm search-based software engineering(SBSE) Aquila optimizer(AO) test case prioritization(TCP) average percentage of fault detection(APFD) average percentage of block coverage(APBC) average percentage of decision coverage(APDC)
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基于SABO-NARX的非线性结构损伤建模 认领 引用
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作者 申向英 李少义 +4 位作者 陈汉新 王雷 王志刚 梁晓培 史朝辉 《机床与液压》 北大核心 2026年第7期195-200,共6页
传统非线性自回归外生输入(NARX)模型在结构损伤检测中,因模型参数选择不稳定,常导致参数辨识精度较低,进而影响损伤检测的准确性。为了解决这一问题,基于SABO算法对NARX模型参数进行辨识,通过迭代优化自动搜索最优参数组合,实现模型结... 传统非线性自回归外生输入(NARX)模型在结构损伤检测中,因模型参数选择不稳定,常导致参数辨识精度较低,进而影响损伤检测的准确性。为了解决这一问题,基于SABO算法对NARX模型参数进行辨识,通过迭代优化自动搜索最优参数组合,实现模型结构与参数的协同优化,从而提升预测精度和鲁棒性。进一步,利用模型参数对不同损伤结构非线性动力学特性的敏感性,实现对结构损伤程度的有效识别。实验结果表明:SABO算法通过动态调整搜索方向,显著提升了参数辨识精度。优化后的模型参数对不同损伤程度的铝合金薄板呈现出明显的区分度:随着裂纹不断加深,模型参数亦呈增大趋势。与基于AIC准则的模型相比,SABO优化后的模型在预测精度和损伤区分度上均表现出显著优势。Bootstrap验证进一步表明:模型在非裂纹区与裂纹区的拟合度(R 2>90%)均保持较高水平,且置信区间重叠显著,验证了该方法良好的鲁棒性,为结构健康监测提供了一种兼具高精度和强鲁棒性的新途径。 展开更多
关键词 损伤识别 NARX模型 SABO算法 非线性动力学建模
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A Comprehensive Survey on Snake Optimizer and Its Performance Evaluation in Image Clustering Field 认领 引用
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作者 Rebika Rai Totan Bharasa +1 位作者 Arunita Das Krishna Gopal Dhal 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期185-251,共67页
Snake Optimizer(SO)is a popular optimization algorithm developed by Hashim and Hussien,based on the competitive and selective mating nature of snakes.By emulating such natural methods,SO presents an intelligent method... Snake Optimizer(SO)is a popular optimization algorithm developed by Hashim and Hussien,based on the competitive and selective mating nature of snakes.By emulating such natural methods,SO presents an intelligent method to solve complicated optimization problems,making it a valuable tool in various scientific and technological applications.This paper provides an extensive review of the SO,its inception,the development of different variants,and applications.This paper identifies several SO variants,such as improved SO variants using different strategies,hybridized SO variants with other metaheuristics,Binary SO variants to solve discrete optimization problems,and multi-objective SO variants to tackle many objectives.Furthermore,the applications of variants of SO demonstrate its adaptability across diverse fields.In addition,the paper discusses a few of the possible future research directions for SO.The performance of the SO has been evaluated in the clustering-based image segmentation domain and compared to other MAs.The numerical and statistical results clearly demonstrate the superiority of the SO to other tested MAs.With researchers engaging MA as an alternate methodology in solving almost every optimization challenge,this survey would definitely provide valuable perceptions to numerous researchers seeking to attain a thorough understanding of SO,its advancements,and its broad applications in resolving diverse optimization problems. 展开更多
关键词 Snake optimizer optimization metaheuristic swarm intelligence nature-inspired image segmentation clustering
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Multi-Objective Enhanced Cheetah Optimizer for Joint Optimization of Computation Offloading and Task Scheduling in Fog Computing 认领 引用
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作者 Ahmad Zia Nazia Azim +5 位作者 Bekarystankyzy Akbayan Khalid J.Alzahrani Ateeq Ur Rehman Faheem Ullah Khan Nouf Al-Kahtani Hend Khalid Alkahtani 《Computers, Materials & Continua》 SCIE EI 2026年第3期1559-1588,共30页
The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous c... The cloud-fog computing paradigm has emerged as a novel hybrid computing model that integrates computational resources at both fog nodes and cloud servers to address the challenges posed by dynamic and heterogeneous computing networks.Finding an optimal computational resource for task offloading and then executing efficiently is a critical issue to achieve a trade-off between energy consumption and transmission delay.In this network,the task processed at fog nodes reduces transmission delay.Still,it increases energy consumption,while routing tasks to the cloud server saves energy at the cost of higher communication delay.Moreover,the order in which offloaded tasks are executed affects the system’s efficiency.For instance,executing lower-priority tasks before higher-priority jobs can disturb the reliability and stability of the system.Therefore,an efficient strategy of optimal computation offloading and task scheduling is required for operational efficacy.In this paper,we introduced a multi-objective and enhanced version of Cheeta Optimizer(CO),namely(MoECO),to jointly optimize the computation offloading and task scheduling in cloud-fog networks to minimize two competing objectives,i.e.,energy consumption and communication delay.MoECO first assigns tasks to the optimal computational nodes and then the allocated tasks are scheduled for processing based on the task priority.The mathematical modelling of CO needs improvement in computation time and convergence speed.Therefore,MoECO is proposed to increase the search capability of agents by controlling the search strategy based on a leader’s location.The adaptive step length operator is adjusted to diversify the solution and thus improves the exploration phase,i.e.,global search strategy.Consequently,this prevents the algorithm from getting trapped in the local optimal solution.Moreover,the interaction factor during the exploitation phase is also adjusted based on the location of the prey instead of the adjacent Cheetah.This increases the exploitation capability of agents,i.e.,local search capability.Furthermore,MoECO employs a multi-objective Pareto-optimal front to simultaneously minimize designated objectives.Comprehensive simulations in MATLAB demonstrate that the proposed algorithm obtains multiple solutions via a Pareto-optimal front and achieves an efficient trade-off between optimization objectives compared to baseline methods. 展开更多
关键词 Computation offloading task scheduling cheetah optimizer fog computing optimization resource allocation internet of things
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Grey Wolf Optimizer for Cluster-Based Routing in Wireless Sensor Networks:A Methodological Survey 认领 引用
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作者 Mohammad Shokouhifar Fakhrosadat Fanian +4 位作者 Mehdi Hosseinzadeh Aseel Smerat Kamal M.Othman Abdulfattah Noorwali Esam Y.O.Zafar 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期191-255,共65页
Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these netw... Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these networks continue to grow in scale and complexity,the need for energy-efficient,scalable,and robust communication protocols becomes more critical than ever.Metaheuristic algorithms have shown significant promise in addressing these challenges,offering flexible and effective solutions for optimizing WSN performance.Among them,the Grey Wolf Optimizer(GWO)algorithm has attracted growing attention due to its simplicity,fast convergence,and strong global search capabilities.Accordingly,this survey provides an in-depth review of the applications of GWO and its variants for clustering,multi-hop routing,and hybrid cluster-based routing in WSNs.We categorize and analyze the existing GWO-based approaches across these key network optimization tasks,discussing the different problem formulations,decision variables,objective functions,and performance metrics used.In doing so,we examine standard GWO,multi-objective GWO,and hybrid GWO models that incorporate other computational intelligence techniques.Each method is evaluated based on how effectively it addresses the core constraints of WSNs,including energy consumption,communication overhead,and network lifetime.Finally,this survey outlines existing gaps in the literature and proposes potential future research directions aimed at enhancing the effectiveness and real-world applicability of GWO-based techniques for WSN clustering and routing.Our goal is to provide researchers and practitioners with a clear,structured understanding of the current state of GWO in WSNs and inspire further innovation in this evolving field. 展开更多
关键词 Wireless sensor networks data transmission energy efficiency lifetime clustering routing optimization metaheuristic algorithms grey wolf optimizer
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A Comprehensive Review of Barnacles Mating Optimizer:Theoretical Foundation,Variants,Applications,and Future Research Directions 认领 引用
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作者 Mohammed A.El-Shorbagy Anas Bouaouda Fatma A.Hashim 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第5期41-118,共78页
As real-world optimization problems become more complex,the development of sophisticated and robust algorithms has become essential.Consequently,researchers are focusing on advanced optimizationmethods that efficientl... As real-world optimization problems become more complex,the development of sophisticated and robust algorithms has become essential.Consequently,researchers are focusing on advanced optimizationmethods that efficiently explore the feasible solution space.This involves designing new high-performance algorithms or enhancing existing meta-heuristic methods by integrating advanced evolutionary strategies.Barnacles Mating Optimizer(BMO)is an evolutionary-basedmeta-heuristic algorithminspired by themating behavior of barnacles,incorporating Hardy–Weinberg principles and the sperm-cast mechanism.Introduced in 2020,BMO has attracted significant attention and has been successfully applied across diverse fields due to its simple design,ease of implementation,high flexibility,and efficient convergence.Therefore,this review provides an overview and synthesis of studies employing BMO.It begins with an introduction to BMO,describing its natural inspiration and optimization framework,followed by a discussion of its core operational procedures and theoretical foundations.The paper then presents a comprehensive analysis of recent BMO variants,systematically categorizing them into modified,multi-objective,and hybrid versions.It also examines BMO’s diverse real-world applications,including power and control engineering,classification,image processing,wireless networks,forecasting,and signal processing.In addition,an updated performance evaluation of BMO is provided,comparing its effectiveness against recently published algorithms using the CEC2005 benchmark suite.Key strengths of BMO are highlighted,including its ability to balance exploration and exploitation,adaptability across problem domains,and its potential for hybridization with other optimization algorithms.Finally,potential enhancements and future research directions are outlined,including multi-objective variants,integration with deep learning,and parallel or distributed implementations. 展开更多
关键词 Evolutionary algorithms barnacles mating optimizer meta-heuristics engineering optimization computational intelligence
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A Parallelized Grey Wolf Optimizer-Based Fuzzy C-Means for Fast and Accurate MRI Segmentation on GPU 认领 引用
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作者 Mohammed Debakla Ali Mezaghrani +1 位作者 Khalifa Djemal Imane Zouaneb 《Computers, Materials & Continua》 SCIE EI 2026年第2期668-688,共21页
Magnetic Resonance Imaging(MRI)has a pivotal role in medical image analysis,for its ability in supporting disease detection and diagnosis.Fuzzy C-Means(FCM)clustering is widely used for MRI segmentation due to its abi... Magnetic Resonance Imaging(MRI)has a pivotal role in medical image analysis,for its ability in supporting disease detection and diagnosis.Fuzzy C-Means(FCM)clustering is widely used for MRI segmentation due to its ability to handle image uncertainty.However,the latter still has countless limitations,including sensitivity to initialization,susceptibility to local optima,and high computational cost.To address these limitations,this study integrates Grey Wolf Optimization(GWO)with FCM to enhance cluster center selection,improving segmentation accuracy and robustness.Moreover,to further refine optimization,Fuzzy Entropy Clustering was utilized for its distinctive features from other traditional objective functions.Fuzzy entropy effectively quantifies uncertainty,leading to more well-defined clusters,improved noise robustness,and better preservation of anatomical structures in MRI images.Despite these advantages,the iterative nature of GWO and FCM introduces significant computational overhead,which restricts their applicability to high-resolution medical images.To overcome this bottleneck,we propose a Parallelized-GWO-based FCM(P-GWO-FCM)approach using GPU acceleration,where both GWO optimization and FCM updates(centroid computation and membership matrix updates)are parallelized.By concurrently executing these processes,our approach efficiently distributes the computational workload,significantly reducing execution time while maintaining high segmentation accuracy.The proposed parallel method,P-GWO-FCM,was evaluated on both simulated and clinical brain MR images,focusing on segmenting white matter,gray matter,and cerebrospinal fluid regions.The results indicate significant improvements in segmentation accuracy,achieving a Jaccard Similarity(JS)of 0.92,a Partition Coefficient Index(PCI)of 0.91,a Partition Entropy Index(PEI)of 0.25,and a Davies-Bouldin Index(DBI)of 0.30.Experimental comparisons demonstrate that P-GWO-FCM outperforms existing methods in both segmentation accuracy and computational efficiency,making it a promising solution for real-time medical image segmentation. 展开更多
关键词 Grey wolf optimizer FCM GPU parallel MRI segmentation
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基于GSABO-ICEEMDAN-KELM的局部放电识别方法在气体绝缘开关设备故障诊断中的应用 认领 引用 被引量：2
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作者 王思涵 马宏忠 +2 位作者 孙维 葛威 陈悦林 《南方电网技术》 CSCD 北大核心 2026年第2期66-77,共12页
气体绝缘开关(gas-insulated switchgear,GIS)设备在生产运行时存在多种绝缘缺陷,准确识别绝缘缺陷导致的局部放电信号对保障GIS设备及电力系统安全有重大意义。采用融合黄金正弦算法(golden sine algorithm,Golden-SA)改进减法优化(sub... 气体绝缘开关(gas-insulated switchgear,GIS)设备在生产运行时存在多种绝缘缺陷,准确识别绝缘缺陷导致的局部放电信号对保障GIS设备及电力系统安全有重大意义。采用融合黄金正弦算法(golden sine algorithm,Golden-SA)改进减法优化(subtraction-average-based optimizer,SABO)算法,得到了融合黄金正弦改进SABO优化算法(GSABO),对改进的完全自适应噪声集合经验模态分解(improved complete ensemble empirical mode decomposition with adaptive noise)与核极限学习机(kernel extreme learning machine)进行参数寻优,以实现对GIS局部放电故障的识别。首先,针对SABO可能陷入局部最优、收敛速度不够理想等问题,引入混沌映射与黄金正弦对其进行改进。然后,搭建实验平台采集4种典型局部放电信号,利用GSABO-ICEEMDAN对其进行分解,并利用相关系数法筛选有效的模态分量。最后计算筛选后模态分量的样本熵形成特征矩阵,将其输入GSABO-KELM进行故障分类识别。通过实验分析表明,相比于未改进的SABO算法,GSABO在跳出局部最优、收敛速度与精度上有明显的优势。结合其他传统算法进行对比,GSABO-ICEEMDAN-KELM的识别准确率可达99.1667%,验证了此算法的准确性与优越性,对于GIS局部放电故障诊断的工程应用具有参考意义。 展开更多
关键词 气体绝缘组合电器 局部放电 ICEEMDAN 改进减法优化算法 黄金正弦算法 核极限学习机 故障诊断
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Adaptive Enhanced Grey Wolf Optimizer for Efficient Cluster Head Selection and Network Lifetime Maximization in Wireless Sensor Networks 认领 引用
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作者 Omar Almomani Mahran Al-Zyoud +3 位作者 Ahmad Adel Abu-Shareha Ammar Almomani Said A.Salloum Khaled Mohammad Alomari 《Computers, Materials & Continua》 SCIE EI 2026年第5期784-813,共30页
In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe ... In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe energy constraints inWSNs.This paper presents an Adaptive Enhanced GreyWolf Optimizer(AEGWO)for energy-efficient cluster head(CH)selection that mitigates the exploration–exploitation imbalance,preserves population diversity,and avoids premature convergence inherent in baseline GWO.The AEGWO combines adaptive control of the parameter of the search pressure to accelerate convergence without stagnation,a hybrid velocity-momentum update based on the dynamics of PSO,and an intelligent mutation operator to maintain the diversity of the population.The search is guided by a multi-objective fitness,which aims at maximizing the residual energy,equal distribution of CH,minimizing the intra-cluster distance,desirable proximity to sinks,and enhancing the coverage.Simulations on 100 nodes homogeneousWSN Tested the proposed AEGWO under the same conditions with LEACH,GWO,IGWO,PSO,WOA,and GA,AEGWO significantly increases stability and lifetime compared to LEACHand other tested algorithms;it has the best first,half,and last node dead,and higher residual energy and smaller communication overhead.The findings prove that AEGWO provides sustainable energy management and better lifetime extension,which makes it a robust,flexible clustering protocol of large-scaleWSNs. 展开更多
关键词 Wireless sensor networks energy efficiency cluster head selection grey wolf optimizer
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KBGWO-RNP:Knowledge-Based GreyWolf Optimizer for Multi-Criteria RFID Network Planning in Medical Asset Monitoring 认领 引用
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作者 Mohamad Khairi Ishak Samir Ait Lhadj Lamin +4 位作者 Mohammad Shokouhifar Aseel Smerat Kamal M.Othman Abdulfattah Noorwali Esam Y.O.Zafar 《Computers, Materials & Continua》 SCIE EI 2026年第9期2253-2282,共30页
Radio Frequency Identification(RFID)has emerged as an effective remote technology for real-time monitoring and management of medical assets in hospitals.Most existing RFID Network Planning(RNP)methods are primarily ba... Radio Frequency Identification(RFID)has emerged as an effective remote technology for real-time monitoring and management of medical assets in hospitals.Most existing RFID Network Planning(RNP)methods are primarily based on either heuristic or metaheuristic approaches.While heuristic approaches are computationally efficient and converge rapidly,they often suffer from premature convergence and suboptimal network configurations.Conversely,metaheuristic algorithms provide stronger global search capabilities and improved solution quality,but they typically require higher computational effort and may still exhibit stagnation in local optima when applied to complex hospital layouts.To overcome these limitations while utilizing the strengths of both paradigms,this paper proposes a Knowledge-Based Grey Wolf Optimizer for RNP,referred to as KBGWO-RNP.The proposed method integrates the global exploration capability of the metaheuristic-driven search with knowledge-based heuristic operators that guide local search and refinement.In particular,the framework incorporates domain-specific knowledge to enhance antenna placement decisions and improve convergence behavior.The KBGWO-RNP framework supports directional antennas with varying coverage profiles.A multi-criteria objective function is formulated to increase the network coverage while simultaneously reducing inter-antenna interference and deployment cost.Extensive simulation experiments conducted on a hospital layout demonstrate that the proposed KBGWO-RNP framework consistently outperforms conventional heuristic and metaheuristic baselines.The results show that the proposed method achieves a coverage rate of 90.4%while maintaining the interference level at 19.9%,indicating a strong balance between performance different objectives.Furthermore,ablation analysis confirms that the integration of knowledge-based guidance with metaheuristic search significantly improves both solution quality and stability.The proposed framework offers a balanced trade-off between computational efficiency and optimization performance,and demonstrating clear advantages over existing approaches. 展开更多
关键词 RFID network planning(RNP) medical asset tracking coverage interference heuristic information grey wolf optimizer(GWO)
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