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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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Wheat Leaf Disease Detection with ANOVA-Driven Feature Selection and Whale Optimization Algorithm 认领 引用
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作者 M Chilakarao Santi Kumari Behera +1 位作者 Ashoka Kumar Ratha Prabira Kumar Sethy 《Journal of Harbin Institute of Technology(New Series)》 CAS 2026年第4期73-84,共12页
Early and precise identification of wheat leaf diseases is crucial for sustainable crop management and yield improvement.In this study,we propose a novel hybrid framework that combines deep feature extraction(using Re... Early and precise identification of wheat leaf diseases is crucial for sustainable crop management and yield improvement.In this study,we propose a novel hybrid framework that combines deep feature extraction(using ResNet50 and VGG16)with ANOVA-driven feature selection and whale optimization algorithm(WOA)for hyperparameter tuning of support vector machine(SVM)classifiers.The dataset consists of 40285 wheat leaf images across eight classes(seven disease types and healthy leaves),including augmented samples to address class imbalance.The analysis of variance(ANOVA)method significantly reduced dimensionality by selecting the top 500 most relevant features,while the WOA fine-tuned the SVM to enhance classification performance.The proposed model achieved an impressive accuracy of 98.1%,along with a precision of 97.9%,a recall of 98.0%,and an F1-score of 98.0%on the independent test set.A comparative analysis shows that our method outperforms several state-of-the-art(SOTA)models,including standard CNN and ensemble approaches.This study demonstrates that combining statistical feature selection and bio-inspired optimization with deep learning can substantially advance automated wheat leaf disease detection,offering promising applications for precision agriculture. 展开更多
关键词 wheat leaf disease classification deep learning ResNet50 VGG16 support vector machine ANOVA feature selection whale optimization algorithm
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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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Improved coati optimization algorithm through multi-strategy integration:from theoretical design to engineering applications 认领 引用
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作者 Shuangxi LIU Ruizhe FENG +2 位作者 Yuxin WEI Wei HUANG Binbin YAN 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2025年第12期1197-1210,共14页
Optimization problems are crucial for a wide range of engineering applications,as efficient solutions lead to better performance.This study introduces an improved coati optimization algorithm(ICOA)that overcomes the p... Optimization problems are crucial for a wide range of engineering applications,as efficient solutions lead to better performance.This study introduces an improved coati optimization algorithm(ICOA)that overcomes the primary limitations of the original coati optimization algorithm(COA),notably its insufficient population diversity and propensity to become trapped in local optima.To address these issues,the ICOA integrates three innovative strategies:Latin hypercube sampling(LHS),Lévyflight,and an adaptive local search.LHS is employed to ensure a diverse initial population,thereby laying a foundation for the optimization.Lévy-flight is utilized to facilitate an efficient global search,enhancing the algorithm’s ability to explore the solution space.The adaptive local search is designed to refine solutions,enabling more precise local exploration.Together,these strategies significantly improve the population’s quality and diversity,thereby improving the algorithm’s convergence accuracy and optimization capabilities.The performance of the ICOA is tested against several established algorithms,using 12 benchmark functions.Additionally,the ICOA’s practicality and effectiveness are demonstrated through application to a real-world engineering problem,specifically the design optimization of tension/compression springs.Simulation results show that the ICOA consistently outperforms the other algorithms,providing robust solutions for a wide range of optimization problems. 展开更多
关键词 Improved coati optimization algorithm(ICOA) Latin hypercube sampling(LHS) Lévy-flight Adaptive local search Multi-strategy Engineering applications
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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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DDoS Attack Autonomous Detection Model Based on Multi-Strategy Integrate Zebra Optimization Algorithm 认领 引用
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作者 Chunhui Li Xiaoying Wang +2 位作者 Qingjie Zhang Jiaye Liang Aijing Zhang 《Computers, Materials & Continua》 SCIE EI 2025年第1期645-674,共30页
Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convol... Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural Networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low accuracy.Therefore,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 dataset.The hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 dataset.The evaluation accuracy of the new dataset GINKS2023 created in this paper is 0.9386.Compared to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score. 展开更多
关键词 Distributed denial of service attack intrusion detection deep learning zebra optimization algorithm multi-strategy integrated zebra optimization algorithm
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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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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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Multi-strategy hybrid whale optimization algorithms for complex constrained optimization problems 认领 引用
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作者 王振宇 WANG Lei 《High Technology Letters》 EI CAS 2024年第1期99-108,共10页
A multi-strategy hybrid whale optimization algorithm(MSHWOA)for complex constrained optimization problems is proposed to overcome the drawbacks of easily trapping into local optimum,slow convergence speed and low opti... A multi-strategy hybrid whale optimization algorithm(MSHWOA)for complex constrained optimization problems is proposed to overcome the drawbacks of easily trapping into local optimum,slow convergence speed and low optimization precision.Firstly,the population is initialized by introducing the theory of good point set,which increases the randomness and diversity of the population and lays the foundation for the global optimization of the algorithm.Then,a novel linearly update equation of convergence factor is designed to coordinate the abilities of exploration and exploitation.At the same time,the global exploration and local exploitation capabilities are improved through the siege mechanism of Harris Hawks optimization algorithm.Finally,the simulation experiments are conducted on the 6 benchmark functions and Wilcoxon rank sum test to evaluate the optimization performance of the improved algorithm.The experimental results show that the proposed algorithm has more significant improvement in optimization accuracy,convergence speed and robustness than the comparison algorithm. 展开更多
关键词 whale optimization algorithm(WOA) good point set nonlinear convergence factor siege mechanism
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Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection 认领 引用 被引量：1
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作者 Deng Yang Chong Zhou +2 位作者 Xuemeng Wei Zhikun Chen Zheng Zhang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第8期1563-1593,共31页
In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature sel... In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:http://gffzz188fe103f8f1460as9cb6n6p0w5kp6xfw.ffgz.tsg.suse.edu.cn/zc0315/MSMOWOA. 展开更多
关键词 Multi-objective optimization whale optimization algorithm multi-strategy feature selection
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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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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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Multi-trial Vector-based Whale Optimization Algorithm 认领 引用 被引量：2
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作者 Mohammad H.Nadimi-Shahraki Hajar Farhanginasab +2 位作者 Shokooh Taghian Ali Safaa Sadiq Seyedali Mirjalili 《Journal of Bionic Engineering》 SCIE EI CSCD 2024年第3期1465-1495,共31页
The Whale Optimization Algorithm(WOA)is a swarm intelligence metaheuristic inspired by the bubble-net hunting tactic of humpback whales.In spite of its popularity due to simplicity,ease of implementation,and a limited... The Whale Optimization Algorithm(WOA)is a swarm intelligence metaheuristic inspired by the bubble-net hunting tactic of humpback whales.In spite of its popularity due to simplicity,ease of implementation,and a limited number of parameters,WOA’s search strategy can adversely affect the convergence and equilibrium between exploration and exploitation in complex problems.To address this limitation,we propose a new algorithm called Multi-trial Vector-based Whale Optimization Algorithm(MTV-WOA)that incorporates a Balancing Strategy-based Trial-vector Producer(BS_TVP),a Local Strategy-based Trial-vector Producer(LS_TVP),and a Global Strategy-based Trial-vector Producer(GS_TVP)to address real-world optimization problems of varied degrees of difficulty.MTV-WOA has the potential to enhance exploitation and exploration,reduce the probability of being stranded in local optima,and preserve the equilibrium between exploration and exploitation.For the purpose of evaluating the proposed algorithm's performance,it is compared to eight metaheuristic algorithms utilizing CEC 2018 test functions.Moreover,MTV-WOA is compared with well-stablished,recent,and WOA variant algorithms.The experimental results demonstrate that MTV-WOA surpasses comparative algorithms in terms of the accuracy of the solutions and convergence rate.Additionally,we conducted the Friedman test to assess the gained results statistically and observed that MTV-WOA significantly outperforms comparative algorithms.Finally,we solved five engineering design problems to demonstrate the practicality of MTV-WOA.The results indicate that the proposed MTV-WOA can efficiently address the complexities of engineering challenges and provide superior solutions that are superior to those of other algorithms. 展开更多
关键词 Swarm intelligence algorithms Metaheuristic algorithms Optimization Engineering design problems Whale optimization algorithm
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 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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Optimization of Adaptive Fuzzy Controller for Maximum Power Point Tracking Using Whale Algorithm 认领 引用
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作者 Mehrdad Ahmadi Kamarposhti Hassan Shokouhandeh +1 位作者 Ilhami Colak Kei Eguchi 《Computers, Materials & Continua》 SCIE EI 2022年第12期5041-5061,共21页
The advantage of fuzzy controllers in working with inaccurate and nonlinear inputs is that there is no need for an accurate mathematical model and fast convergence and minimal fluctuations in the maximum power point d... The advantage of fuzzy controllers in working with inaccurate and nonlinear inputs is that there is no need for an accurate mathematical model and fast convergence and minimal fluctuations in the maximum power point detector.The capability of online fuzzy tracking systems is maximum power,resistance to radiation and temperature changes,and no need for external sensors to measure radiation intensity and temperature.However,the most important issue is the constant changes in the amount of sunlight that cause the maximum power point to be constantly changing.The controller used in the maximum power point tracking(MPPT)circuit must be able to adapt to the new radiation conditions.Therefore,in this paper,to more accurately track the maximumpower point of the solar system and receive more electrical power at its output,an adaptive fuzzy control was proposed,the parameters of which are optimized by the whale algorithm.The studies have repeated under different irradiation conditions and the proposed controller performance has been compared with perturb and observe algorithm(P&O)method,which is a practical and high-performance method.To evaluate the performance of the proposed algorithm,the particle swarm algorithm optimized the adaptive fuzzy controller.The simulation results show that the adaptive fuzzy control system performs better than the P&O tracking system.Higher accuracy and consequently more production power at the output of the solar panel is one of the salient features of the proposed control method,which distinguishes it from other methods.On the other hand,the adaptive fuzzy controller optimized by the whale algorithm has been able to perform relatively better than the controller designed by the particle swarm algorithm,which confirms the higher accuracy of the proposed algorithm. 展开更多
关键词 Maximum power tracking photovoltaic system adaptive fuzzy control whale optimization algorithm particle swarm optimization
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An Efficient Hybrid Model Based on Modified Whale Optimization Algorithm and Multilayer Perceptron Neural Network for Medical Classification Problems 认领 引用 被引量：1
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作者 Saeid Raziani Sajad Ahmadian +1 位作者 Seyed Mohammad Jafar Jalali Abdolah Chalechale 《Journal of Bionic Engineering》 SCIE EI CSCD 2022年第5期1504-1521,共18页
Feedforward Neural Network(FNN)is one of the most popular neural network models that is utilized to solve a wide range of nonlinear and complex problems.Several models such as stochastic gradient descent have been dev... Feedforward Neural Network(FNN)is one of the most popular neural network models that is utilized to solve a wide range of nonlinear and complex problems.Several models such as stochastic gradient descent have been developed to train FNNs.However,they mainly suffer from falling into local optima leading to reduce the accuracy of FNNs.Moreover,the convergence speed of training process depends on the initial values of weights and biases in FNNs.Generally,these values are randomly determined by most of the training models.To deal with these issues,in this paper,we develop a novel evolutionary algorithm by modifying the original version of Whale Optimization Algorithm(WOA).To this end,a nonlinear function is introduced to improve the exploration and exploitation phases in the search process of WOA.Then,the modified WOA is applied to automatically obtain the initial values of weights and biases in FNN leading to reduce the probability of falling into local optima.In addition,the FNN model trained by the modified WOA is used to develop a classification approach for medical diagnosis problems.Ten medical diagnosis datasets are utilized to evaluate the efficiency of the proposed method.Also,four evaluation metrics including accuracy,AUC,specificity,and sensitivity are used in the experiments to compare the performance of classification models.The experimental results demonstrate that the proposed method is better than other competing classification models due to achieving higher values of accuracy,AUC,specificity,and sensitivity metrics for the used datasets. 展开更多
关键词 Feed forward neural network Meta-heuristic algorithm Whale optimization algorithm Optimization Classification Bionic algorithm
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