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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 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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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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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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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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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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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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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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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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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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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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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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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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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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Improved Gain Shared Knowledge Optimizer Based Reactive Power Optimization for Various Renewable Penetrated Power Grids with Static Var Generator Participation 认领 引用
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作者 Xuan Ruan HanYan +4 位作者 DonglinHu Min Zhang YingLi DiHai Bo Yang 《Energy Engineering》 EI 2026年第3期23-56,共34页
An optimized volt-ampere reactive(VAR)control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale... An optimized volt-ampere reactive(VAR)control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale wind/solar farms with shunt static var generators(SVGs).The model explicitly represents reactive-power regulation characteristics of doubly-fed wind turbines and PV inverters under real-time meteorological conditions,and quantifies SVG high-speed compensation capability,enabling seamless transition from localized VAR management to a globally coordinated strategy.An enhanced adaptive gain-sharing knowledge optimizer(AGSK-SD)integrates simulated annealing and diversity maintenance to autonomously tune voltage-control actions,renewable source reactive-power set-points,and SVG output.The algorithm adaptively modulates knowledge factors and ratios across search phases,performs SA-based fine-grained local exploitation,and periodically re-injects population diversity to prevent premature convergence.Comprehensive tests on IEEE 9-bus and 39-bus systems demonstrate AGSK-SD’s superiority over NSGA-II and MOPSO in hypervolume(HV),inverse generative distance(IGD),and spread metrics while maintaining acceptable computational burden.The method reduces network losses from 2.7191 to 2.15 MW(20.79%reduction)and from 15.1891 to 11.22 MW(26.16%reduction)in the 9-bus and 39-bus systems respectively.Simultaneously,the cumulative voltage-deviation index decreases from 0.0277 to 3.42×10−4 p.u.(98.77%reduction)in the 9-bus system,and from 0.0556 to 0.0107 p.u.(80.76%reduction)in the 39-bus system.These improvements demonstrate significant suppression of line losses and voltage fluctuations.Comparative analysis with traditional heuristic optimization algorithms confirms the superior performance of the proposed approach. 展开更多
关键词 Gained-sharing knowledge improved algorithm adaptive parameter adjustment simulated annealing local search algorithms diversity enhancement mechanisms wind and solar new energy static var generator reactive power optimization
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Collaborative Trajectory Planning for Stereoscopic Agricultural Multi-UAVs Driven by the Aquila Optimizer 认领 引用 被引量：2
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作者 Xinyu Liu Longfei Wang +1 位作者 Yuxin Ma Peng Shao 《Computers, Materials & Continua》 SCIE EI 2025年第1期1349-1376,共28页
Stereoscopic agriculture,as an advanced method of agricultural production,poses new challenges for multi-task trajectory planning of unmanned aerial vehicles(UAVs).To address the need for UAVs to perform multi-task tr... Stereoscopic agriculture,as an advanced method of agricultural production,poses new challenges for multi-task trajectory planning of unmanned aerial vehicles(UAVs).To address the need for UAVs to perform multi-task trajectory planning in stereoscopic agriculture,a multi-task trajectory planning model and algorithm(IEP-AO)that synthesizes flight safety and flight efficiency is proposed.Based on the requirements of stereoscopic agricultural geomorphological features and operational characteristics,the multi-task trajectory planning model is ensured by constructing targeted constraints at five aspects,including the path,slope,altitude,corner,energy and obstacle threat,to improve the effectiveness of the trajectory planning model.And combined with the path optimization algorithm,an Aquila optimizer(IEP-AO)based on the interference-enhanced combination model is proposed,which can help UAVs to improve the trajectory search capability in complex operation space and large-scale operation tasks,and jump out of the locally optimal trajectory path region timely,to generate the optimal trajectory planning plan that can adapt to the diversity of the tasks and the flight efficiency.Meanwhile,four simulated flights with different operation scales and different scene constraints were conducted under the constructed real 3Dimension scene,and the experimental results can show that the proposedmulti-task trajectory planning method canmeet themulti-task requirements in stereoscopic agriculture and improve the mission execution efficiency and agricultural production effect of UAV. 展开更多
关键词 Stereoscopic agriculture unmanned aerial vehicle multi-task interference model Aquila optimizer
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Harnessing TLBO-Enhanced Cheetah Optimizer for Optimal Feature Selection in Cancer Data 认领 引用
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作者 Bibhuprasad Sahu Amrutanshu Panigrahi +5 位作者 Abhilash Pati Ashis Kumar Pati Janmejaya Mishra Naim Ahmad Salman Arafath Mohammed Saurav Mallik 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第10期1029-1054,共26页
Metaheuristic optimization methods are iterative search processes that aim to efficiently solve complexoptimization problems. These basically find the solution space very efficiently, often without utilizing the gradi... Metaheuristic optimization methods are iterative search processes that aim to efficiently solve complexoptimization problems. These basically find the solution space very efficiently, often without utilizing the gradientinformation, and are inspired by the bio-inspired and socially motivated heuristics. Metaheuristic optimizationalgorithms are increasingly applied to complex feature selection problems in high-dimensional medical datasets.Among these, Teaching-Learning-Based optimization (TLBO) has proven effective for continuous design tasks bybalancing exploration and exploitation phases. However, its binary version (BTLBO) suffers from limited exploitationability, often converging prematurely or getting trapped in local optima, particularly when applied to discrete featureselection tasks. Previous studies reported that BTLBO yields lower classification accuracy and higher feature subsetvariance compared to other hybrid methods in benchmark tests, motivating the development of hybrid approaches.This study proposes a novel hybrid algorithm, BTLBO-Cheetah Optimizer (BTLBO-CO), which integrates the globalexploration strength of BTLBO with the local exploitation efficiency of the Cheetah Optimization (CO) algorithm. Theobjective is to enhance the feature selection process for cancer classification tasks involving high-dimensional data. Theproposed BTLBO-CO algorithm was evaluated on six benchmark cancer datasets: 11 tumors (T), Lung Cancer (LUC),Leukemia (LEU), Small Round Blue Cell Tumor or SRBCT (SR), Diffuse Large B-cell Lymphoma or DLBCL (DL), andProstate Tumor (PT).The results demonstrate superior classification accuracy across all six datasets, achieving 93.71%,96.12%, 98.13%, 97.11%, 98.44%, and 98.84%, respectively.These results validate the effectiveness of the hybrid approachin addressing diverse feature selection challenges using a Support Vector Machine (SVM) classifier. 展开更多
关键词 Cancer classification hybrid model teaching-learning-based optimization cheetah optimizer feature selection
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LOEV-APO-MLP:Latin Hypercube Opposition-Based Elite Variation Artificial Protozoa Optimizer for Multilayer Perceptron Training 认领 引用
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作者 Zhiwei Ye Dingfeng Song +7 位作者 Haitao Xie Jixin Zhang Wen Zhou Mengya Lei Xiao Zheng Jie Sun Jing Zhou Mengxuan Li 《Computers, Materials & Continua》 SCIE EI 2025年第12期5509-5530,共22页
The Multilayer Perceptron(MLP)is a fundamental neural network model widely applied in various domains,particularly for lightweight image classification,speech recognition,and natural language processing tasks.Despite ... The Multilayer Perceptron(MLP)is a fundamental neural network model widely applied in various domains,particularly for lightweight image classification,speech recognition,and natural language processing tasks.Despite its widespread success,training MLPs often encounter significant challenges,including susceptibility to local optima,slow convergence rates,and high sensitivity to initial weight configurations.To address these issues,this paper proposes a Latin Hypercube Opposition-based Elite Variation Artificial Protozoa Optimizer(LOEV-APO),which enhances both global exploration and local exploitation simultaneously.LOEV-APO introduces a hybrid initialization strategy that combines Latin Hypercube Sampling(LHS)with Opposition-Based Learning(OBL),thus improving the diversity and coverage of the initial population.Moreover,an Elite Protozoa Variation Strategy(EPVS)is incorporated,which applies differential mutation operations to elite candidates,accelerating convergence and strengthening local search capabilities around high-quality solutions.Extensive experiments are conducted on six classification tasks and four function approximation tasks,covering a wide range of problem complexities and demonstrating superior generalization performance.The results demonstrate that LOEV-APO consistently outperforms nine state-of-the-art metaheuristic algorithms and two gradient-based methods in terms of convergence speed,solution accuracy,and robustness.These findings suggest that LOEV-APO serves as a promising optimization tool for MLP training and provides a viable alternative to traditional gradient-based methods. 展开更多
关键词 Artificial protozoa optimizer multilayer perceptron Latin hypercube sampling opposition-based learning neural network training
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Multivariate natural gas price forecasting model with feature selection,machine learning and chernobyl disaster optimizer 认领 引用
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作者 Pei Du Xuan-Kai Zhang +1 位作者 Jun-Tao Du Jian-Zhou Wang 《Petroleum Science》 SCIE EI CAS CSCD 2025年第11期4823-4837,共15页
The significance of accurately forecasting natural gas prices is far-reaching and significant,not only for the stable operation of the energy market,but also as a key element in promoting sustainable development and a... The significance of accurately forecasting natural gas prices is far-reaching and significant,not only for the stable operation of the energy market,but also as a key element in promoting sustainable development and addressing environmental challenges.However,natural gas prices are affected by multiple source factors,presenting complex,unstable nonlinear characteristics hindering the improvement of the prediction accuracy of existing models.To address this issue,this study proposes an innovative multivariate combined forecasting model for natural gas prices.Initially,the study meticulously identifies and introduces 16 variables impacting natural gas prices across five crucial dimensions:the production,marketing,commodities,political and economic indicators of the United States and temperature.Subsequently,this study employs the least absolute shrinkage and selection operator,grey relation analysis,and random forest for dimensionality reduction,effectively screening out the most influential key variables to serve as input features for the subsequent learning model.Building upon this foundation,a suite of machine learning models is constructed to ensure precise natural gas price prediction.To further elevate the predictive performance,an intelligent algorithm for parameter optimization is incorporated,addressing potential limitations of individual models.To thoroughly assess the prediction accuracy of the proposed model,this study conducts three experiments using monthly natural gas trading prices.These experiments incorporate 19 benchmark models for comparative analysis,utilizing five evaluation metrics to quantify forecasting effectiveness.Furthermore,this study conducts in-depth validation of the proposed model's effectiveness through hypothesis testing,discussions on the improvement ratio of forecasting performance,and case studies on other energy prices.The empirical results demonstrate that the multivariate combined forecasting method developed in this study surpasses other comparative models in forecasting accuracy.It offers new perspectives and methodologies for natural gas price forecasting while also providing valuable insights for other energy price forecasting studies. 展开更多
关键词 Natural gas price forecasting Multivariate forecasting model Machine learning Chernobyl disaster optimizer
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Design of Chaos Induced Aquila Optimizer for Parameter Estimation of Electro-Hydraulic Control System 认领 引用
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作者 Khizer Mehmood Naveed Ishtiaq Chaudhary +4 位作者 Zeshan Aslam Khan Khalid Mehmood Cheema Muhammad Asif Zahoor Raja Sultan SAlshamrani Kaled MAlshmrany 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第5期1809-1841,共33页
Aquila Optimizer(AO)is a recently proposed population-based optimization technique inspired by Aquila’s behavior in catching prey.AO is applied in various applications and its numerous variants were proposed in the l... Aquila Optimizer(AO)is a recently proposed population-based optimization technique inspired by Aquila’s behavior in catching prey.AO is applied in various applications and its numerous variants were proposed in the literature.However,chaos theory has not been extensively investigated in AO.Moreover,it is still not applied in the parameter estimation of electro-hydraulic systems.In this work,ten well-defined chaotic maps were integrated into a narrowed exploitation of AO for the development of a robust chaotic optimization technique.An extensive investigation of twenty-three mathematical benchmarks and ten IEEE Congress on Evolutionary Computation(CEC)functions shows that chaotic Aquila optimization techniques perform better than the baseline technique.The investigation is further conducted on parameter estimation of an electro-hydraulic control system,which is performed on various noise levels and shows that the proposed chaotic AO with Piecewise map(CAO6)achieves the best fitness values of and at noise levels and respectively.Friedman test 2.873E-05,1.014E-04,8.728E-031.300E-03,1.300E-02,1.300E-01,for repeated measures,computational analysis,and Taguchi test reflect the superiority of CAO6 against the state of the arts,demonstrating its potential for addressing various engineering optimization problems.However,the sensitivity to parameter tuning may limit its direct application to complex optimization scenarios. 展开更多
关键词 Aquila optimizer electro-hydraulic control system chaos theory autoregressive model
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