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Predictive modeling for mechanical properties of cold-rolled strip steel based on random forest regression and whale optimization algorithm 认领 引用
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作者 Hong-Lei Cai Yi-Ming Fang +3 位作者 Le Liu Li-Hui Ren Zhen-Dong Liu Xiao-Dong Zhao 《Journal of Iron and Steel Research International》 SCIE EI CSCD 2026年第3期73-87,共15页
In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method n... In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method named RFR-WOA is developed based on random forest regression(RFR)and whale optimization algorithm(WOA).Firstly,using Pearson and Spearman correlation analysis and Gini coefficient importance ranking on an actual production dataset containing 37,878 samples,22 key variables are selected as model inputs from 112 variables that affect mechanical properties.Subsequently,an RFR-based predictive model for the mechanical properties of cold-rolled strip steel is constructed.Then,with the combination of the coefficient of determination(R2)and root mean square error as the optimization objective,the hyperparameters of RFR model are iteratively optimized using WOA,and better predictive effectiveness is obtained.Finally,the mechanical properties prediction model based on RFR-WOA is compared with models established using deep neural networks,convolutional neural networks,and other methods.The test results on 9469 samples of actual production data show that the model developed present has better predictive accuracy and generalization capability. 展开更多
关键词 Cold-rolled strip steel Mechanical property Predictive modeling Random forest regression Whale optimization algorithm
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GSLDWOA: A Feature Selection Algorithm for Intrusion Detection Systems in IIoT 认领 引用
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作者 Wanwei Huang Huicong Yu +3 位作者 Jiawei Ren Kun Wang Yanbu Guo Lifeng Jin 《Computers, Materials & Continua》 SCIE EI 2026年第1期2006-2029,共24页
Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from... Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity.These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy.This paper proposes an industrial Internet ofThings intrusion detection feature selection algorithm based on an improved whale optimization algorithm(GSLDWOA).The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to,such as local optimality,long detection time,and reduced accuracy.First,the initial population’s diversity is increased using the Gaussian Mutation mechanism.Then,Non-linear Shrinking Factor balances global exploration and local development,avoiding premature convergence.Lastly,Variable-step Levy Flight operator and Dynamic Differential Evolution strategy are introduced to improve the algorithm’s search efficiency and convergence accuracy in highdimensional feature space.Experiments on the NSL-KDD and WUSTL-IIoT-2021 datasets demonstrate that the feature subset selected by GSLDWOA significantly improves detection performance.Compared to the traditional WOA algorithm,the detection rate and F1-score increased by 3.68%and 4.12%.On the WUSTL-IIoT-2021 dataset,accuracy,recall,and F1-score all exceed 99.9%. 展开更多
关键词 Industrial Internet of Things intrusion detection system feature selection whale optimization algorithm Gaussian mutation
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Improved Whale Optimization-Based Neural Network Predictive Control for Industrial Refrigeration Systems 认领 引用
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作者 Qi Li Menghan Yang +1 位作者 Shifa Cui Kun Han 《Journal of Harbin Institute of Technology(New Series)》 CAS 2026年第3期1-14,共14页
The inherent nonlinearity and time-delay characteristics of industrial refrigeration processes complicate parameter tuning for conventional PID control,adversely affecting its precision.This makes the control of such ... The inherent nonlinearity and time-delay characteristics of industrial refrigeration processes complicate parameter tuning for conventional PID control,adversely affecting its precision.This makes the control of such systems a significant and challenging research problem.To address this,a novel Neural Network Predictive Control(NNPC)algorithm is proposed,which integrates an Improved Deep Belief Network(IDBN)with an Improved Whale Optimization Algorithm(IWOA).First,the IDBN acts as a high-precision nonlinear prediction model,significantly improving multi-step prediction accuracy.Second,the IWOA is employed to optimize the predictive controller,featuring three major improvements:an improved population initialization,a modified convergence factor update mechanism,and an added disturbance strategy,which collectively accelerate convergence and enhance global search capability.Finally,simulation results demonstrate that the proposed NNPC algorithm achieves superior set-point tracking performance and strong robustness against external disturbances. 展开更多
关键词 industrial refrigeration systems model predictive control time⁃delay whale optimization algorithm deep belief network
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Hybrid Spotted Hyena and Whale Optimization Algorithm-Based Dynamic Load Balancing Technique for Cloud Computing Environment 认领 引用
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作者 N Jagadish Kumar R Praveen +1 位作者 D Selvaraj D Dhinakaran 《China Communications》 SCIE EI CSCD 2025年第8期206-227,共22页
The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is n... The uncertain nature of mapping user tasks to Virtual Machines(VMs) causes system failure or execution delay in Cloud Computing.To maximize cloud resource throughput and decrease user response time,load balancing is needed.Possible load balancing is needed to overcome user task execution delay and system failure.Most swarm intelligent dynamic load balancing solutions that used hybrid metaheuristic algorithms failed to balance exploitation and exploration.Most load balancing methods were insufficient to handle the growing uncertainty in job distribution to VMs.Thus,the Hybrid Spotted Hyena and Whale Optimization Algorithm-based Dynamic Load Balancing Mechanism(HSHWOA) partitions traffic among numerous VMs or servers to guarantee user chores are completed quickly.This load balancing approach improved performance by considering average network latency,dependability,and throughput.This hybridization of SHOA and WOA aims to improve the trade-off between exploration and exploitation,assign jobs to VMs with more solution diversity,and prevent the solution from reaching a local optimality.Pysim-based experimental verification and testing for the proposed HSHWOA showed a 12.38% improvement in minimized makespan,16.21% increase in mean throughput,and 14.84% increase in network stability compared to baseline load balancing strategies like Fractional Improved Whale Social Optimization Based VM Migration Strategy FIWSOA,HDWOA,and Binary Bird Swap. 展开更多
关键词 cloud computing load balancing Spotted Hyena Optimization Algorithm(SHOA) throughput Virtual Machines(VMs) Whale Optimization Algorithm(WOA)
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Two-stage wind-solar-storage capacity allocation method research for active distribution network based on whale migrating algorithm 认领 引用
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作者 Wu Zheng Ting Tang +1 位作者 Xianggang He Jierui Yang 《Global Energy Interconnection》 EI CSCD 2026年第3期555-566,共12页
With the integration of clean energy,the increasing penetration of distributed power sources,controllable loads,and energy-storage resources in smart grids is causing substantial difficulties in the safe,stable,and co... With the integration of clean energy,the increasing penetration of distributed power sources,controllable loads,and energy-storage resources in smart grids is causing substantial difficulties in the safe,stable,and cost-effective operation of power systems.Existing research often fails to consider the interconnections among optimal dispatching and the distribution capacities of wind,solar,and energy-storage systems(ESSs).This increases the costs and dispatching difficulties.In response to this situation,a two-stage capacity-allocation approach for wind and solar power and storage in an active distribution network(ADN)is proposed in this paper.This approach is founded based on the whale migration algorithm(WMA).First,an optimization dispatch model that considers controllable loads and energy storage is formulated to minimize the dispatch operation costs of the ADN.In this optimization model,the overall cost of the ADN is taken as the objective function.The optimal configuration for wind–solar–storage capacities is obtained through the WMA.Simulation results confirm that the WMA effectively balances the solution accuracy and computational efficiency,while the proposed scheme enhances the economic performance of active distribution grids. 展开更多
关键词 Active distribution network(ADN) Optimization dispatching Capacity allocation Two-stage Whale migration algorithm(WMA)
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A Sine and Wormhole Energy Whale Optimization Algorithm for Optimal FACTS Placement in Uncertain Wind Integrated Scenario Based Power Systems 认领 引用
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作者 Sunilkumar P.Agrawal Pradeep Jangir +4 位作者 Arpita Sundaram B.Pandya Anil Parmar Ahmad O.Hourani Bhargavi Indrajit Trivedi 《Journal of Bionic Engineering》 SCIE EI CSCD 2025年第4期2115-2134,共20页
The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACT... The Sine and Wormhole Energy Whale Optimization Algorithm(SWEWOA)represents an advanced solution method for resolving Optimal Power Flow(OPF)problems in power systems equipped with Flexible AC Transmission System(FACTS)devices which include Thyristor-Controlled Series Compensator(TCSC),Thyristor-Controlled Phase Shifter(TCPS),and Static Var Compensator(SVC).SWEWOA expands Whale Optimization Algorithm(WOA)through the integration of sine and wormhole energy features thus improving exploration and exploitation capabilities for efficient convergence in complex non-linear OPF problems.A performance evaluation of SWEWOA takes place on the IEEE-30 bus test system through static and dynamic loading scenarios where it demonstrates better results than five contemporary algorithms:Adaptive Chaotic WOA(ACWOA),WOA,Chaotic WOA(CWOA),Sine Cosine Algorithm Differential Evolution(SCADE),and Hybrid Grey Wolf Optimization(HGWO).The research shows that SWEWOA delivers superior generation cost reduction than other algorithms by reaching a minimum of 0.9%better performance.SWEWOA demonstrates superior power loss performance by achieving(Ploss,min)at the lowest level compared to all other tested algorithms which leads to better system energy efficiency.The dynamic loading performance of SWEWOA leads to a 4.38%reduction in gross costs which proves its capability to handle different operating conditions.The algorithm achieves top performance in Friedman Rank Test(FRT)assessments through multiple performance metrics which verifies its consistent reliability and strong stability during changing power demands.The repeated simulations show that SWEWOA generates mean costs(Cgen,min)and mean power loss values(Ploss,min)with small deviations which indicate its capability to maintain cost-effective solutions in each simulation run.SWEWOA demonstrates great potential as an advanced optimization solution for power system operations through the results presented in this study. 展开更多
关键词 Sine and wormhole energy whale optimization algorithm(SWEWOA) Optimal power flow(OPF) Wind integration FACTS devices Power system optimization
Energy Efficient Clustering and Sink Mobility Protocol Using Hybrid Golden Jackal and Improved Whale Optimization Algorithm for Improving Network Longevity in WSNs 认领 引用
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作者 S B Lenin R Sugumar +2 位作者 J S Adeline Johnsana N Tamilarasan R Nathiya 《China Communications》 SCIE EI CSCD 2025年第3期16-35,共20页
Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability... Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability.In this paper,Hybrid Golden Jackal,and Improved Whale Optimization Algorithm(HGJIWOA)is proposed as an effective and optimal routing protocol that guarantees efficient routing of data packets in the established between the CHs and the movable sink.This HGJIWOA included the phases of Dynamic Lens-Imaging Learning Strategy and Novel Update Rules for determining the reliable route essential for data packets broadcasting attained through fitness measure estimation-based CH selection.The process of CH selection achieved using Golden Jackal Optimization Algorithm(GJOA)completely depends on the factors of maintainability,consistency,trust,delay,and energy.The adopted GJOA algorithm play a dominant role in determining the optimal path of routing depending on the parameter of reduced delay and minimal distance.It further utilized Improved Whale Optimisation Algorithm(IWOA)for forwarding the data from chosen CHs to the BS via optimized route depending on the parameters of energy and distance.It also included a reliable route maintenance process that aids in deciding the selected route through which data need to be transmitted or re-routed.The simulation outcomes of the proposed HGJIWOA mechanism with different sensor nodes confirmed an improved mean throughput of 18.21%,sustained residual energy of 19.64%with minimized end-to-end delay of 21.82%,better than the competitive CH selection approaches. 展开更多
关键词 Cluster Heads(CHs) Golden Jackal Optimization Algorithm(GJOA) Improved Whale Optimization Algorithm(IWOA) unequal clustering
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Research on the Optimal Scheduling Model of Energy Storage Plant Based on Edge Computing and Improved Whale Optimization Algorithm 认领 引用
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作者 Zhaoyu Zeng Fuyin Ni 《Energy Engineering》 EI 2025年第3期1153-1174,共22页
Energy storage power plants are critical in balancing power supply and demand.However,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device ... Energy storage power plants are critical in balancing power supply and demand.However,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device energy utilization.To tackle these challenges,this study proposes an optimal scheduling model for energy storage power plants based on edge computing and the improved whale optimization algorithm(IWOA).The proposed model designs an edge computing framework,transferring a large share of data processing and storage tasks to the network edge.This architecture effectively reduces transmission costs by minimizing data travel time.In addition,the model considers demand response strategies and builds an objective function based on the minimization of the sum of electricity purchase cost and operation cost.The IWOA enhances the optimization process by utilizing adaptive weight adjustments and an optimal neighborhood perturbation strategy,preventing the algorithm from converging to suboptimal solutions.Experimental results demonstrate that the proposed scheduling model maximizes the flexibility of the energy storage plant,facilitating efficient charging and discharging.It successfully achieves peak shaving and valley filling for both electrical and heat loads,promoting the effective utilization of renewable energy sources.The edge-computing framework significantly reduces transmission delays between energy devices.Furthermore,IWOA outperforms traditional algorithms in optimizing the objective function. 展开更多
关键词 Energy storage plant edge computing optimal energy scheduling improved whale optimization algorithm
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MCWOA Scheduler:Modified Chimp-Whale Optimization Algorithm for Task Scheduling in Cloud Computing 认领 引用 被引量：1
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作者 Chirag Chandrashekar Pradeep Krishnadoss +1 位作者 Vijayakumar Kedalu Poornachary Balasundaram Ananthakrishnan 《Computers, Materials & Continua》 SCIE EI 2024年第2期2593-2616,共24页
Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay ... Cloud computing provides a diverse and adaptable resource pool over the internet,allowing users to tap into various resources as needed.It has been seen as a robust solution to relevant challenges.A significant delay can hamper the performance of IoT-enabled cloud platforms.However,efficient task scheduling can lower the cloud infrastructure’s energy consumption,thus maximizing the service provider’s revenue by decreasing user job processing times.The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm(MCWOA),combines elements of the Chimp Optimization Algorithm(COA)and the Whale Optimization Algorithm(WOA).To enhance MCWOA’s identification precision,the Sobol sequence is used in the population initialization phase,ensuring an even distribution of the population across the solution space.Moreover,the traditional MCWOA’s local search capabilities are augmented by incorporating the whale optimization algorithm’s bubble-net hunting and random search mechanisms into MCWOA’s position-updating process.This study demonstrates the effectiveness of the proposed approach using a two-story rigid frame and a simply supported beam model.Simulated outcomes reveal that the new method outperforms the original MCWOA,especially in multi-damage detection scenarios.MCWOA excels in avoiding false positives and enhancing computational speed,making it an optimal choice for structural damage detection.The efficiency of the proposed MCWOA is assessed against metrics such as energy usage,computational expense,task duration,and delay.The simulated data indicates that the new MCWOA outpaces other methods across all metrics.The study also references the Whale Optimization Algorithm(WOA),Chimp Algorithm(CA),Ant Lion Optimizer(ALO),Genetic Algorithm(GA)and Grey Wolf Optimizer(GWO). 展开更多
关键词 Cloud computing scheduling chimp optimization algorithm whale optimization algorithm
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An Improved Whale Algorithm and Its Application in Truss Optimization 认领 引用 被引量：9
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作者 Fengguo Jiang Lutong Wang Lili Bai 《Journal of Bionic Engineering》 SCIE EI CSCD 2021年第3期721-732,共12页
The current Whale Optimization Algorithm(WOA)has several drawbacks,such as slow convergence,low solution accuracy and easy to fall into the local optimal solution.To overcome these drawbacks,an improved Whale Optimiza... The current Whale Optimization Algorithm(WOA)has several drawbacks,such as slow convergence,low solution accuracy and easy to fall into the local optimal solution.To overcome these drawbacks,an improved Whale Optimization Algorithm(IWOA)is proposed in this study.IWOA can enhance the global search capability by two measures.First,the crossover and mutation operations in Differential Evolutionary algorithm(DE)are combined with the whale optimization algorithm.Second,the cloud adaptive inertia weight is introduced in the position update phase of WOA to divide the population into two subgroups,so as to balance the global search ability and local development ability.ANSYS and Matlab are used to establish the structure model.To demonstrate the application of the IWOA,truss structural optimizations on 52-bar plane truss and 25-bar space truss were performed,and the results were are compared with that obtained by other optimization algorithm.It is verified that,compared with WOA,the IWOA has higher efficiency,fast convergence speed,better solution accuracy and stability.So IWOA can be used in the optimization design of large truss structures. 展开更多
关键词 improve whale optimization algorithm differential evolutionary algorithm cloud theory simulating optimization bionic algorithm
A Whale Optimization Algorithm with Distributed Collaboration and Reverse Learning Ability 认领 引用 被引量：8
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作者 Zhedong Xu Yongbo Su +1 位作者 Fang Yang Ming Zhang 《Computers, Materials & Continua》 SCIE EI 2023年第6期5965-5986,共22页
Due to the development of digital transformation,intelligent algorithms are getting more and more attention.The whale optimization algorithm(WOA)is one of swarm intelligence optimization algorithms and is widely used ... Due to the development of digital transformation,intelligent algorithms are getting more and more attention.The whale optimization algorithm(WOA)is one of swarm intelligence optimization algorithms and is widely used to solve practical engineering optimization problems.However,with the increased dimensions,higher requirements are put forward for algorithm performance.The double population whale optimization algorithm with distributed collaboration and reverse learning ability(DCRWOA)is proposed to solve the slow convergence speed and unstable search accuracy of the WOA algorithm in optimization problems.In the DCRWOA algorithm,the novel double population search strategy is constructed.Meanwhile,the reverse learning strategy is adopted in the population search process to help individuals quickly jump out of the non-ideal search area.Numerical experi-ments are carried out using standard test functions with different dimensions(10,50,100,200).The optimization case of shield construction parameters is also used to test the practical application performance of the proposed algo-rithm.The results show that the DCRWOA algorithm has higher optimization accuracy and stability,and the convergence speed is significantly improved.Therefore,the proposed DCRWOA algorithm provides a better method for solving practical optimization problems. 展开更多
关键词 Whale optimization algorithm double population cooperation distribution reverse learning convergence speed
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基于IWOA优化TCN模型的磨煤机故障预警研究 认领 引用 被引量：4
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作者 杨婷婷 罗海玉 +1 位作者 李浩千 吕游 《华北电力大学学报（自然科学版）》 CAS 北大核心 2026年第3期121-128,138,共8页
为了提前发现和预知磨煤机可能发生的故障,提出了一种基于改进鲸鱼算法(IWOA)优化时间卷积网络(TCN)的磨煤机故障预警方法。首先选择能够表征磨煤机故障的7个变量,建立基于时间卷积网络的预测模型。然后引入非线性收敛因子和自适应权重... 为了提前发现和预知磨煤机可能发生的故障,提出了一种基于改进鲸鱼算法(IWOA)优化时间卷积网络(TCN)的磨煤机故障预警方法。首先选择能够表征磨煤机故障的7个变量,建立基于时间卷积网络的预测模型。然后引入非线性收敛因子和自适应权重系数来改进鲸鱼优化算法,并将其用于预测模型超参数的优化,利用优化后模型的预测值和真实值构造偏离度函数,并采用区间估计法来确定每一时刻的自适应预警阈值。最后将所提算法用于某660 MW火电机组中速磨煤机故障预警中,结果表明相较于LSTM、CNN-LSTM、TCN模型,IWOA-TCN模型可以精确预测各变量的变化趋势,提前6 h 16 min发现异常,实现故障预警。 展开更多
关键词 磨煤机 故障预警 时间卷积网络 鲸鱼优化算法 自适应阈值
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An Improved Whale Optimization Algorithm for Feature Selection 认领 引用 被引量：4
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作者 Wenyan Guo Ting Liu +1 位作者 Fang Dai Peng Xu 《Computers, Materials & Continua》 SCIE EI 2020年第1期337-354,共18页
Whale optimization algorithm(WOA)is a new population-based meta-heuristic algorithm.WOA uses shrinking encircling mechanism,spiral rise,and random learning strategies to update whale’s positions.WOA has merit in term... Whale optimization algorithm(WOA)is a new population-based meta-heuristic algorithm.WOA uses shrinking encircling mechanism,spiral rise,and random learning strategies to update whale’s positions.WOA has merit in terms of simple calculation and high computational accuracy,but its convergence speed is slow and it is easy to fall into the local optimal solution.In order to overcome the shortcomings,this paper integrates adaptive neighborhood and hybrid mutation strategies into whale optimization algorithms,designs the average distance from itself to other whales as an adaptive neighborhood radius,and chooses to learn from the optimal solution in the neighborhood instead of random learning strategies.The hybrid mutation strategy is used to enhance the ability of algorithm to jump out of the local optimal solution.A new whale optimization algorithm(HMNWOA)is proposed.The proposed algorithm inherits the global search capability of the original algorithm,enhances the exploitation ability,improves the quality of the population,and thus improves the convergence speed of the algorithm.A feature selection algorithm based on binary HMNWOA is proposed.Twelve standard datasets from UCI repository test the validity of the proposed algorithm for feature selection.The experimental results show that HMNWOA is very competitive compared to the other six popular feature selection methods in improving the classification accuracy and reducing the number of features,and ensures that HMNWOA has strong search ability in the search feature space. 展开更多
关键词 Whale optimization algorithm Filter and Wrapper model K-nearest neighbor method Adaptive neighborhood hybrid mutation
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AWK-TIS:An Improved AK-IS Based on Whale Optimization Algorithm and Truncated Importance Sampling for Reliability Analysis 认领 引用 被引量：2
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作者 Qiang Qin Xiaolei Cao Shengpeng Zhang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第5期1457-1480,共24页
In this work,an improved active kriging method based on the AK-IS and truncated importance sampling(TIS)method is proposed to efficiently evaluate structural reliability.The novel method called AWK-TIS is inspired by ... In this work,an improved active kriging method based on the AK-IS and truncated importance sampling(TIS)method is proposed to efficiently evaluate structural reliability.The novel method called AWK-TIS is inspired by AK-IS and RBF-GA previously published in the literature.The innovation of the AWK-TIS is that TIS is adopted to lessen the sample pool size significantly,and the whale optimization algorithm(WOA)is employed to acquire the optimal Krigingmodel and themost probable point(MPP).To verify the performance of theAWK-TISmethod for structural reliability,four numerical cases which are utilized as benchmarks in literature and one real engineering problem about a jet van manipulate mechanism are tested.The results indicate the accuracy and efficiency of the proposed method. 展开更多
关键词 Structural reliability active kriging whale optimization algorithm AK-IS
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An Optimal Node Localization in WSN Based on Siege Whale Optimization Algorithm 认领 引用 被引量：2
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作者 Thi-Kien Dao Trong-The Nguyen 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第3期2201-2237,共37页
Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging andfundamental operations in various monitoring or tracking applications because the network deploys a large areaand... Localization or positioning scheme in Wireless sensor networks (WSNs) is one of the most challenging andfundamental operations in various monitoring or tracking applications because the network deploys a large areaand allocates the acquired location information to unknown devices. The metaheuristic approach is one of themost advantageous ways to deal with this challenging issue and overcome the disadvantages of the traditionalmethods that often suffer from computational time problems and small network deployment scale. This studyproposes an enhanced whale optimization algorithm that is an advanced metaheuristic algorithm based on thesiege mechanism (SWOA) for node localization inWSN. The objective function is modeled while communicatingon localized nodes, considering variables like delay, path loss, energy, and received signal strength. The localizationapproach also assigns the discovered location data to unidentified devices with the modeled objective functionby applying the SWOA algorithm. The experimental analysis is carried out to demonstrate the efficiency of thedesigned localization scheme in terms of various metrics, e.g., localization errors rate, converges rate, and executedtime. Compared experimental-result shows that theSWOA offers the applicability of the developed model forWSNto perform the localization scheme with excellent quality. Significantly, the error and convergence values achievedby the SWOA are less location error, faster in convergence and executed time than the others compared to at least areduced 1.5% to 4.7% error rate, and quicker by at least 4%and 2% in convergence and executed time, respectivelyfor the experimental scenarios. 展开更多
关键词 Node localization whale optimization algorithm wireless sensor networks siege whale optimization algorithm optimization
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An Improved Whale Optimization Algorithm for Global Optimization and Realized Volatility Prediction 认领 引用 被引量：1
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作者 Xiang Wang Liangsa Wang +1 位作者 Han Li Yibin Guo 《Computers, Materials & Continua》 SCIE EI 2023年第12期2935-2969,共35页
The original whale optimization algorithm(WOA)has a low initial population quality and tends to converge to local optimal solutions.To address these challenges,this paper introduces an improved whale optimization algo... The original whale optimization algorithm(WOA)has a low initial population quality and tends to converge to local optimal solutions.To address these challenges,this paper introduces an improved whale optimization algorithm called OLCHWOA,incorporating a chaos mechanism and an opposition-based learning strategy.This algorithm introduces chaotic initialization and opposition-based initialization operators during the population initialization phase,thereby enhancing the quality of the initial whale population.Additionally,including an elite opposition-based learning operator significantly improves the algorithm’s global search capabilities during iterations.The work and contributions of this paper are primarily reflected in two aspects.Firstly,an improved whale algorithm with enhanced development capabilities and a wide range of application scenarios is proposed.Secondly,the proposed OLCHWOA is used to optimize the hyperparameters of the Long Short-Term Memory(LSTM)networks.Subsequently,a prediction model for Realized Volatility(RV)based on OLCHWOA-LSTM is proposed to optimize hyperparameters automatically.To evaluate the performance of OLCHWOA,a series of comparative experiments were conducted using a variety of advanced algorithms.These experiments included 38 standard test functions from CEC2013 and CEC2019 and three constrained engineering design problems.The experimental results show that OLCHWOA ranks first in accuracy and stability under the same maximum fitness function calls budget.Additionally,the China Securities Index 300(CSI 300)dataset is used to evaluate the effectiveness of the proposed OLCHWOA-LSTM model in predicting RV.The comparison results with the other eight models show that the proposed model has the highest accuracy and goodness of fit in predicting RV.This further confirms that OLCHWOA effectively addresses real-world optimization problems. 展开更多
关键词 Whale optimization algorithm chaos mechanism opposition-based learning long short-term memory realized volatility
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基于IWOA-BP的红松人工林枯落针叶层火蔓延速率预测模型 认领 引用 被引量：1
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作者 黄天棋 辛颖 张敏 《南京林业大学学报（自然科学版）》 CAS CSCD 北大核心 2026年第2期29-36,共8页
【目的】红松(Pinus koraiensis)针叶油脂含量较高,存在极高的森林火灾风险,地表火蔓延是其主要的火灾传播方式。本研究通过构建地表火蔓延速率预测模型,为红松人工林的火灾防控提供科学依据。【方法】以黑龙江省凉水地区红松人工林枯... 【目的】红松(Pinus koraiensis)针叶油脂含量较高,存在极高的森林火灾风险,地表火蔓延是其主要的火灾传播方式。本研究通过构建地表火蔓延速率预测模型,为红松人工林的火灾防控提供科学依据。【方法】以黑龙江省凉水地区红松人工林枯落针叶层为材料,进行松针含水率为0、5%、10%、15%、20%,坡度为0、5°、10°、15°,风速为0、1、2、3、4、5 m/s的360组室内点烧试验,根据热电偶法测定火蔓延速率,构建改进鲸鱼优化算法(IWOA)-BP神经网络模型对火蔓延速率进行预测,并与3种模型(WOA-BP神经网络、GA-BP神经网络和PSO-BP神经网络)进行预测结果对比。【结果】坡度、风速与火蔓延速度均呈极显著正相关(P<0.01),含水率与火蔓延速度呈显著负相关(P<0.05);火蔓延速率随可燃物含水率的增加而降低,随风速和坡度的增加而升高,在风速为4 m/s时,火蔓延增长速率达到最大值。IWOA算法引入Tent混沌映射、改进非线性收敛因子、增加自适应权重和Levy飞行运动,增加了算法的随机性和多样性,提高了收敛速度,同时避免陷入局部最优,具备较高预测精度和鲁棒性;IWOA优化的BP神经网络模型精度和稳定性明显高于其他3种模型,对实测数据的模型适应度最佳。【结论】IWOA-BP神经网络模型能有效地预测红松人工林枯落针叶层的火蔓延速率,为林火防控与森林地表凋落物的火蔓延速率预测模型研究提供科学指导。 展开更多
关键词 红松人工林 火蔓延速率 点烧试验 改进鲸鱼优化算法(IWOA)算法 BP神经网络
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Hybrid Flow Shop Rescheduling Approach Based on Hybrid-Driven Mechanism and Improved Multi-Objective WOA 认领 引用
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作者 Feng Lv Xin Xu +1 位作者 Cheng Yang Yixuan Tang 《Computers, Materials & Continua》 SCIE EI 2026年第7期1982-2009,共28页
To ensure an effective disturbance response and maintain continuous production in hybrid flow shops,this paper focuses on the design of a rescheduling method.A rescheduling model is constructed that minimizes the make... To ensure an effective disturbance response and maintain continuous production in hybrid flow shops,this paper focuses on the design of a rescheduling method.A rescheduling model is constructed that minimizes the makespan,total tardiness,and scheme deviation degree.A hybrid rescheduling driving mechanism based on the latest completion time is designed to effectively trigger rescheduling.The Whale Optimization Algorithm(WOA)is improved by integrating the good point set theory,nonlinear control parameter strategy,and Differential Evolution(DE)algorithm.Moreover,non-dominated sorting and a dynamic external archive mechanism based on crowding distance are introduced to make it suitable for multi-objective optimization problems.The superiority of the Improved Multi-objective Whale Optimization Algorithm(IMOWOA)and the effectiveness of the improved mechanisms are verified through comparative experiments and ablation experiments.Taking the final assembly production line of an agricultural machinery equipment enterprise as an example,a rescheduling scheme is generated based on the practical production requirements,which verifies the feasibility and effectiveness of the proposed method. 展开更多
关键词 Hybrid flow shop production disturbance production rescheduling rescheduling driving mechanism improved multi-objective whale optimization algorithm
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基于WOA-SVMD与毫米波雷达的灾后生命体征检测 认领 引用
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作者 胡青松 寇志豪 +2 位作者 张元生 成元勋 李世银 《煤炭科学技术》 EI CAS CSCD 北大核心 2026年第4期89-99,共11页
矿井巷道空间狭窄、环境复杂,一旦发生灾害事故,被困人员易被埋压。毫米波雷达生命体征检测凭借非接触、抗干扰等优势,在矿山灾难救援中具有不可替代的作用。针对雷达生命体征信号背景噪声干扰强、呼吸谐波与心跳信号频率耦合降低心跳... 矿井巷道空间狭窄、环境复杂,一旦发生灾害事故,被困人员易被埋压。毫米波雷达生命体征检测凭借非接触、抗干扰等优势,在矿山灾难救援中具有不可替代的作用。针对雷达生命体征信号背景噪声干扰强、呼吸谐波与心跳信号频率耦合降低心跳提取精度的问题,提出一种融合鲸鱼优化算法(Whale Optimization Algorithm,WOA)与逐次变分模态分解(Successive Variational Mode Decomposition,SVMD)的生命体征检测方法,核心创新包括:构建参数自适应优化模型,以最大互信息系数(Maximal Information Coefficient,MIC)为适应度函数,利用WOA的全局寻优能力实现SVMD平衡参数的自适应求解,避免经验设置导致的分解偏差,适配矿山复杂环境下信号的动态变化。设计“能量比筛选−相关性系数筛选”两级本征模态分解函数(Intrinsic Mode Function,IMF)筛选机制,通过能量比初步筛选含心跳信息的IMF,再通过相关性系数精准定位最优IMF,提升心跳信号重构精度,满足矿山救援中快速识别存活人员的需求。从测量角度、测量距离、生理状态等维度验证算法适应性与稳定性,为矿山实际救援场景提供支撑。试验结果表明,所提算法的平均绝对误差(Mean Absolute Error,MAE)低至2.71%;与传统带通滤波、经验模态分解、自适应噪声完备经验模态分解及未优化SVMD相比,其分离的心跳信号与真实参考信号相似度提升20.61%~77.01%,可实现复杂条件下心率的高精度、高稳定检测。 展开更多
关键词 生命检测 毫米波雷达 模态分解 鲸鱼优化算法 参数自适应优化
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利用WOA-BP算法的区域GNSS高程异常拟合方法 认领 引用
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作者 李翥 卢献健 +3 位作者 唐长增 宋晓辉 胡鹏程 唐诗华 《桂林理工大学学报》 CAS 北大核心 2026年第1期83-87,共5页
针对区域性GNSS高程拟合模型中选择最佳参数困难的问题,提出了WOA-BP算法的区域GNSS高程异常拟合方法。利用鲸鱼算法(WOA)在全局搜索中寻优能力强、操作简单、调整参数少的特点,为BP神经网络寻找到合适的权值和阈值参数,将优化后的权值... 针对区域性GNSS高程拟合模型中选择最佳参数困难的问题,提出了WOA-BP算法的区域GNSS高程异常拟合方法。利用鲸鱼算法(WOA)在全局搜索中寻优能力强、操作简单、调整参数少的特点,为BP神经网络寻找到合适的权值和阈值参数,将优化后的权值和阈值参数代入到BP神经网络模型中进行训练,从而提高组合算法模型拟合的预测精度和收敛速度。通过实际工程案例中部分GNSS水准重合点数据进行拟合实验,将WOA-BP神经网络算法与BP神经网络、最小二乘支持向量机(LSSVM)法的结果进行对比。结果表明,WOA-BP神经网络模型在使用中具有更强的稳定性、更高的预测精度,可应用于区域GNSS高程拟合的预测。 展开更多
关键词 GNSS高程拟合 BP神经网络 鲸鱼算法
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