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5th International Conference on Computer Engineering and Application (ICCEA)

作者： Li, Zhaodi Liu, Qingqing Yu, Kaixuan Henan Univ Dept Comp & Informat Engn Kaifeng Peoples R China Henan Light Ind Vocat Coll Dept Comp Sci & Art Zhengzhou Peoples R China State Key Lab Shield Machine & Boring Technol Zhengzhou Peoples R China

ISBN: (纸本)9798350386783;9798350386776

In order to better explore a method for solving the problem of electric vehicle charging station location planning, and improve the shortcomings of whale algorithm such as weak stability, sometimes slow convergence speed and easy to fall into local extremum, a whale optimization algorithm with quasi-opposition-based learning and enhanced search mechanism was proposed. Then, the algorithm flow is given, and the CEC2017 test function set is simulated by six representative algorithms in multiple dimensions. The results show that the convergence speed, optimization accuracy and solution stability of the improved algorithm are significantly improved, and it has good convergence performance. In order to solve the problem of electric vehicle public charging station location and service area division, an improved whale algorithm and Voronoi diagram are used to jointly solve the location planning of charging station, so as to achieve the purpose of global optimization. The application results of the last example show that the improved whale algorithm can effectively solve the charging station location problem and has superior optimization performance.

关键词： whale optimization algorithm Enhanced search Location of charging station

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An Enhanced Opposition-Based Golden-Sine whale optimization algorithm 7th

An Enhanced Opposition-Based Golden-Sine Whale Optimization ...

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7th International Conference on Cognitive Computing (ICCC) Held as Part of the Services Conference Federation Meeting (SCF)

作者： Lu, Yong Yi, Chao Li, Jiayun Li, Wentao Minzu Univ China Sch Informat Engn Beijing Peoples R China

ISBN: (纸本)9783031516702;9783031516719

The whale optimization algorithm is an ingenious method conceived by researchers, drawing inspiration from the feeding behavior of humpback whales. Characterized by its simple structure, limited parameters, high efficiency, and robust optimization capacity, WOA has been extensively applied across multiple domains to address various challenges. Nonetheless, it has been found that the algorithm demonstrates low global exploration capability, inadequate search precision, and susceptibility to local optima entrapment. Many enhancements have been suggested in the literature, with Opposition-Based Learning emerging as a particularly effective technique for improving the quality of the initial population. In the present study, we put forth the Enhanced Opposition-Based strategy, which integrates supplementary constraints into the existing Opposition-Based Learning framework, generating a more refined initial population. Furthermore, we introduce the Golden Sine algorithm to modify the optimization approach of WOA, fostering an equilibrium between global exploration and exploitation abilities. In our evaluation, the proposed algorithm is assessed on nine classic benchmark functions with a dimensionality of 500, and compared with the original WOA, An enhanced whale optimization algorithm (eWOA), and the Elite Opposition-Based Golden-Sine whale optimization algorithm (EGolden-SWOA). The results exemplify the superior performance of our proposed algorithm, underscoring its potential application in the optimization of truss structure design problems, the results indicate that ESWOA outperforms other enhanced algorithms, such as eWOA and EGolden-SWOA, in terms of its performance in engineering optimization. This signifies that ESWOA can be effectively applied to engineering optimization problems.

关键词： whale optimization algorithm Opposition-Based Learning Golden Sine algorithm engineering optimization

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Localization of a radioactive source with the predictive range-whale optimization algorithm method

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NUCLEAR ENGINEERING AND TECHNOLOGY 2025年第8期57卷

作者： Hong, Zongheng Cong, Feiyun Yang, Bo Chen, Zhangyu Niu, Yunlong Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Peoples R China Shanghai Inst Special Equipment Supervis & Inspect Shanghai 200062 Peoples R China Zhejiang Ecol Environm Co Ltd Hangzhou 311121 Peoples R China Zhejiang Univ Taizhou Inst Hangzhou 318000 Peoples R China

Heuristic algorithms can effectively improve the accuracy and efficiency of estimating the status of radioactive sources based on the maximum likelihood estimation method. However, heuristic algorithms often converge prematurely at local optima, compromising stability and making them unsuitable for prolonged monitoring tasks. To address this problem, the Predictive Range-whale optimization algorithm (PR-WOA) method was proposed in this paper. Initially, the predictive location and intensity ranges of the radioactive source were determined by integrating historical prediction results. Subsequently, the initial population was more concentrated within the predictive range to improve the algorithm's capability for local optimization. Finally, an inertia weight was introduced to adaptively adjust the search step, consequently improving its global searching capability and efficiency. The performance of the PR-WOA method was evaluated with the simulation and experimental data. Comparative studies demonstrated that the proposed method significantly improves accuracy and stability in predicting the status of the radioactive source. In the 2022 radioactive source localization experiment in Hangzhou, PR-WOA method achieved an average localization accuracy of 1.30m during the trajectory tracking of a moving radioactive source mounted on the drone. Compared to traditional heuristic algorithms, this method improved localization accuracy by 17.9 % and enhanced localization stability during long-term monitoring by 19.0 %.

关键词： Radioactive source Localization Maximum likelihood estimation whale optimization algorithm

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Decision Tree with whale optimization algorithm (DT-WOA) for QoS-based IoT Service Composition 1

Decision Tree with Whale Optimization Algorithm (DT-WOA) for...

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1st International Conference on Electrical, Computer, Telecommunication and Energy Technologies

作者： Ouhadda, Souhila Ait Hacene Sadouki, Samia Chibani Tari, Abdelkamel Univ Bejaia LIMED Lab Fac Exact Sci Bejaia 06000 Algeria Higher Sch Sci & Technol Comp & Digital ESTIN Fac Exact Sci Lab LITAN LIMED Lab RN 75 Amizour Bejaia 06300 Algeria

ISBN: (纸本)9798350388497;9798350388480

From smart homes to smart cities, the Internet of Things (IoT) has revolutionized our lives by connecting billions of devices into an interconnected network. Each smart device offers specific functions, such as remote door unlocking or sending alerts, which we call IoT services. To meet the complex needs of users, combining multiple atomic IoT services is required, as a single service is often insufficient. The increasing number of IoT services offering similar functionality makes service composition based on Quality of Service (QoS) attributes more challenging. To address this issue, we propose a DT-WOA approach combining a Decision Tree (DT) with the whale optimization algorithm (WOA). We use DT-WOA to find a close-to-optimal solution that meets the QoS user requirements. Simulation results indicate that DT-WOA outperforms the original WOA in terms of fitness value by effectively reducing the search space.

关键词： Quality of Service Service Composition Internet of Things Decision Tree whale optimization algorithm Service Selection

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Electrical Parameters Extraction of Diode Using whale optimization algorithm 2

Electrical Parameters Extraction of Diode Using Whale Optimi...

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2nd International Conference on Electrical Engineering and Automatic Control (ICEEAC)

作者： Garoudja, Elyes Filali, Walid Oussalah, Slimane Lekoui, Fouaz Amrani, Rachid Ctr Dev Technol Avancees CDTA Plateforme Technol Microfabricat Cite 20 Aout 1956 Algiers 16081 Algeria Ctr Dev Technol Avancees CDTA Div Microelect & Nanotechnol Cite 20 Aout 1956 Algiers 16081 Algeria Ctr Dev Technol Avancees CDTA Div Milieux Ionises & Laser Cite 20 Aout 1956 Algiers 16081 Algeria Univ Benyoucef Benkhedda Alger 1 Dept Sci Matiere Algiers Algeria

ISBN: (纸本)9798350349740;9798350349757

In this paper, we propose a new method based on an heuristic algorithm, which is called whale optimization algorithm (WOA) to extract the main electrical parameters of a diode, such as saturation current, ideality factor, and series resistance. WOA is an efficient optimization algorithm that imitates the humpback whales natural hunting strategy. Three operators are used to mimic humpback whales behaviors of bubble-net foraging, surrounding, and searching for prey. The findings of the suggested model are compared with analytical and experimental current-voltage data, and they are shown to be in good agreement.

关键词： diode I-V characteristic heuristic algorithm whale optimization algorithm parameters extraction

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An Efficient Hybrid Model Based on Modified whale optimization algorithm and Multilayer Perceptron Neural Network for Medical Classification Problems

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Journal of Bionic Engineering 2022年第5期19卷 1504-1521页

作者： Saeid Raziani Sajad Ahmadian Seyed Mohammad Jafar Jalali Abdolah Chalechale Department of Computer Engineering Razi UniversityKermanshah ***Iran Faculty of Information Technology Kermanshah University of TechnologyKermanshah ***Iran Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsMelbourne 3217Australia

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 *** models such as stochastic gradient descent have been developed to train ***,they mainly suffer from falling into local optima leading to reduce the accuracy of ***,the convergence speed of training process depends on the initial values of weights and biases in ***,these values are randomly determined by most of the training *** 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 ***,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 *** addition,the FNN model trained by the modified WOA is used to develop a classification approach for medical diagnosis *** medical diagnosis datasets are utilized to evaluate the efficiency of the proposed ***,four evaluation metrics including accuracy,AUC,specificity,and sensitivity are used in the experiments to compare the performance of classification *** 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.

关键词： Feed forward neural network Meta-heuristic algorithm whale optimization algorithm optimization Classification Bionic algorithm

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An Enhanced whale optimization algorithm for Task Scheduling in Cloud Computing

An Enhanced Whale Optimization Algorithm for Task Scheduling...

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International Conference on 6G Communications Networking and Signal Processing, SGCNSP 2023

作者： Srilatha, P. Ramadan, Ghazi Mohamad Kumar, T. M. Kiran Alekhya, Y. Pani, Alok Kumar Department of AI & DS Chaitanya Bharathi Institute of Technology Telangana Hyderabad India College of MLT Ahl Al Bayt University Karbala Iraq Department of CSE Siddaganga Institute of Technology Tumakuru India Maisammaguda Hyderabad India Bangalore India

ISBN: (纸本)9789819778751

Task Scheduling is the significant challenge in the environment of Cloud Computing (CC) and has attention in numerous researchers in recent years with respect to attain cost effective computation and improve resource utilization. The existing algorithms has limitations of role and selection criteria of inertia weight was not considered. In this research, Enhanced whale optimization algorithm (EWOA) is proposed for maximize effectiveness of task scheduling in CC. An inertia weight is implemented in WOA algorithm that enhances the convergence and accuracy of algorithm that helps in task scheduling effectiveness. The performance of proposed technique is estimated with performance measure of Makespan (ms), execution time (s) and resource utilization (%). The proposed method attained less execution time of 2304, 2537, 2765, 2983 and 3016 s for 200, 400, 600, 800 and 1000 number of tasks. The proposed method attained the superior results when compared with other existing algorithms like Ant Colony optimization (ACO), Grey Wolf optimization (GWO), Particle Swarm optimization (PSO) and whale optimization algorithm (WOA). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud computing Inertia weight Resource utilization Task scheduling whale optimization algorithm

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Research on selective harmonic elimination technology based on enhanced whale optimization algorithm 21

Research on selective harmonic elimination technology based ...

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21st IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Li, Weijie Zhang, Wenyi Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin Peoples R China

ISBN: (纸本)9798350388084;9798350388077

The key of inverter selective harmonic elimination technology lies in solving nonlinear transcendental equations. This paper takes a two-level three-phase bridge inverter circuit as an example, establishes its mathematical model from its output voltage, then constructs an evaluation function, and verifies the feasibility of the enhanced whale optimization algorithm in a selective harmonic elimination technology through MATLAB/Simulink simulation. At the same time, the simulation results also show that the DC voltage utilization rate of the three-phase inverter controlled by the selective harmonic elimination technology based on the enhanced whale optimization algorithm can reach 1.063, which is superior to the traditional Newton iterative method and SPWM technology in DC voltage utilization rate and harmonic elimination efficiency.

关键词： Inverter selective Harmonic Elimination whale optimization algorithm Nonlinear transcendental equations

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Multi-Mechanism-Driven whale optimization algorithm for Collaborative Scheduling of Industrial Equipment 14

Multi-Mechanism-Driven Whale Optimization Algorithm for Coll...

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14th Asian Control Conference (ASCC)

作者： Lin, Bin Wang, Yalin Tan, Xujie Guan, Lin Qiu, Zhifeng Deconinck, Geert Liu, Chenliang Cent South Univ Sch Automat Changsha Peoples R China Natl Engn Res Ctr Adv Energy Storage Mat Changsha Peoples R China Katholieke Univ Leuven Natl Engn Res Ctr Adv Energy Storage Mat B-3001 Leuven Belgium

ISBN: (纸本)9798331540845;9789887581598

Cranes are typical industrial equipment frequently used in process industries. As the scale of cranes continues to grow, the complexity of their collaborative scheduling problems becomes increasingly prominent. However, traditional optimization algorithms cannot solve these problems well. The dynamic balancing of global search and local exploitation and sensitivity to local optimal solutions in collaborative scheduling problems of industrial equipment present significant hurdles. Hence, this paper proposes a multi-mechanism-driven whale optimization algorithm (MMD-WOA) for establishing a collaborative scheduling model involving multiple cranes operating on the same track. First, a dynamic inertia weight coefficient is devised to balance global search and local exploitation based on environmental cues. Then, a Levy mutation mechanism is integrated to augment the population's capability to escape local optima. After that, a boundary solution repair mechanism is formulated to enhance the utilization and diversity of individual populations. Finally, the effectiveness of the proposed MMD-WOA is validated across 23 benchmark functions and an industrial scheduling problem. The experimental results demonstrate the superior global optimal search performance and robustness of the proposed method.

关键词： Process industry collaborative scheduling whale optimization algorithm dynamic inertia weight coefficient Levy mutation mechanism

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HWOA: an intelligent hybrid whale optimization algorithm for multi-objective task selection strategy in edge cloud computing system

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WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2022年第5期25卷 2265-2295页

作者： Kang, Yan Yang, Xuekun Pu, Bin Wang, Xiaokang Wang, Haining Xu, Yulong Wang, Puming Yunnan Univ Natl Pilot Sch Software Key Lab Software Engn Yunnan Prov Kunming 650500 Yunnan Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410006 Peoples R China Hainan Univ Sch Comp Sci & Technol Haikou 570228 Hainan Peoples R China

Edge computing is a popular computing modality that works by placing computing resources as close as possible to the sensor data to relieve the burden of network bandwidth and data centers in cloud computing. However, as the volume of data and the scale of tasks processed by edge terminals continue to increase, the problem of how to optimize task selection based on execution time with limited computing resources becomes a pressing one. To this end, a hybrid whale optimization algorithm (HWOA) is proposed for multi-objective edge computing task selection. In addition to the execution time of the task, economic profits are also considered to optimize task selection. Specifically, a fuzzy function is designed to address the uncertainty of task's economic profits and execution time. Five interactive constraints among tasks are presented and formulated to improve the performance of task selection. Furthermore, some improved strategies are designed to solve the problem that the whale optimization algorithm (WOA) is subject to local optima entrapment. Finally, an extensive experimental assessment of synthetic datasets is implemented to evaluate the multi-objective optimization performance. Compared with the traditional WOA, the diversity metric (A-spread), the hypervolume (HV) and other evaluation metrics are significantly improved. The experiment results also indicate the proposed approach achieves remarkable performance compared with other competitive methods.

关键词： Edge cloud computing system Multi-objective optimization Scheduling Task selection strategy whale optimization algorithm

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