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检索条件"主题词=Whale optimization algorithms"

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An Analysis of Frequency Response in Integrated Power System Using Power Electronics Controller Tuned With whale optimization Technique

An Analysis of Frequency Response in Integrated Power System...

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Smart Technologies and Systems for Next Generation Computing (ICSTSN), International Conference on

作者： Somnath Ganguly Joon-Ho Choi Joyti Mudi S. Kundu Aloke Kumar Roy V. Mukherjee Department of Electrical Engineering Chonnam National University Gwangju South Korea Bankura Unnayani Insstitute of Engineering Bankura West Bengal India Department of Electrical Engineering Bankura Unnayani Institute of Engineering Bankura West Bengal India Dept. of EEE Bharat Institute of Engineering and Technology Hyderabad India Department of Computer Science and Engineering Bankura Unnayani Institute of Engineering Bankura West Bengal India Department of Electrical Engineering Indian Institute Technology (Indian School of Mines) Dhanbad Jharkhand India

ISBN: (数字)9798350391565

ISBN: (纸本)9798350391572

The performance of any isolated hybrid power system is characterized by the type of controller being used. The considered power generating units are the solar thermal power plant (STPP), wind turbine generator (WTG), and diesel engine generator (DEG), whose frequency stabilization has been done with the utilization of proportional-integral-derivative (PID), integral-tilt-derivative (I-TD), and tilt-integral-derivative (TID) controllers. Tuning controller gains using the whale optimization algorithm (WOA) and the quasi-oppositional based whale optimization algorithm (QOWOA) involves optimizing the parameters to achieve the best performance for a given hybrid system. A comparative frequency response of the system under the individual influence of PID, TID, and I-TD controllers has been shown with different types of load perturbations. The simulation result shows that QOWOA-TID outperforms the other controllers. The study addresses the critical need for optimizing control strategies in hybrid power systems to ensure stability, efficiency, and reliability, thereby contributing to the advancement of sustainable energy solutions.

关键词： Time-frequency analysis Perturbation methods Power system stability whale optimization algorithms Hybrid power systems Generators Frequency response

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Scheduling the Tasks in Cloud Computing using Adaptive Adjustment in whale optimization Algorithm

Scheduling the Tasks in Cloud Computing using Adaptive Adjus...

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Data Science and Information System (ICDSIS), IEEE International Conference on

作者： Piyush Kumar Pareek G D Nithyashree S A Rajashekhar Pankaj Zanke K S Navyashree Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering East West Institute of Technology Bengaluru India Senior Technical Business Analyst Sapiens International Corporation USA

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

One of the most popular infrastructures for employing virtual machines, also known as processing units, is cloud computing. Task scheduling is fundamental problems of cloud computing. A number of methods to handle the nondeterministic polynomial-time (NP)-complete optimisation issue of choosing the best scheduling criteria in cloud computing have been proposed by academics in the past. An effective optimisation approach is suggested in this research, based on the whale optimisation algorithm (WOA), to address the common problems of solution algorithms, which include poor global optimisation ability, ease of sliding into local optimisation, and sluggish convergence speed. We provide an adaptive adjustment method that may dynamically alter search behaviour during the whale optimisation algorithm’s iterative operation. The technique described to further update the position of search individuals also uses the evolutionary approach of differential variation. The algorithm’s exploration and development capacities are then balanced, and a restart approach is included to keep the system from entering local optimisation. The approach simultaneously resolves the work planning issue in the situation of rain cloud processing. When compared to other algorithms, this technique uses more resources and converges more quickly. Additionally, the evaluation function seeks for the optimal answers by taking the makespan, resource cost, and load balancing level into account.

关键词： Cloud computing Scheduling algorithms Heuristic algorithms Scalability whales whale optimization algorithms Scheduling

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Adaptive Inertia Weight with whale optimization Algorithm for Mathematical Models in Energy Saving Efficiency

Adaptive Inertia Weight with Whale Optimization Algorithm fo...

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Intelligent algorithms for Computational Intelligence Systems (IACIS), International Conference on

作者： Lina Ding Zeyu Liu Baotou Light Industry Vocational and Technical College Baotou China

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

The global manufacturing industry there is increasing the recognition of need to enhance energy efficiency to reduce both costs and accuracy. However, achieving accurate energy control is challenging due to the dynamic performance requirements and the need for precise installation of variable displacement pumps or frequency conversion equipment. This paper, proposed Adaptive Inertia Weight with whale optimization Algorithm (AIW-WOA) method is efficiently performed in the energy efficiency in refrigeration plant to achieve better accuracy balance energy. The adaptive mechanism of the inertia weight adjusts based on the iteration process to evaluated the refrigeration of the flexible to changing energy level condition is mathematically represented. The AIW mechanism allow the optimization process to adapt to different operational condition in refrigeration plant such as varying load demand and energy. The proposed AIW-WOA method for energy saving efficient in refrigeration is achieving better outcomes such as accuracy of 92.12% respectively. The existing method such as Genetic Algorithm (GA) and Artificial Neural Network (ANN) and Asymmetric Hydraulic Cylinder (AHC) are evaluated for proposed method.

关键词： Accuracy Energy conservation Artificial neural networks Hydraulic systems Energy efficiency whale optimization algorithms Energy states Refrigeration optimization Genetic algorithms

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Electrical Parameters Extraction of Diode Using whale optimization Algorithm

Electrical Parameters Extraction of Diode Using Whale Optimi...

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Electrical Engineering and Automatic Control (ICEEAC), International Conference on

作者： Elyes Garoudja Walid Filali Slimane Oussalah Fouaz Lekoui Rachid Amrani Plateforme Technologique de Microfabrication Centre de Développement des Technologies Avancées (CDTA) Algiers Algeria Division Microélectronique et Nanotechnologies Centre de Développement des Technologies Avancées (CDTA) Algiers Algeria Division Milieux Ionisés et Laser Centre de Développement des Technologies Avancées(CDTA) Algiers Algeria Département des Sciences de la Matière Université Benyoucef Benkhedda Algiers Algeria

ISBN: (数字)9798350349740

ISBN: (纸本)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： Resistance Semiconductor device modeling Statistical analysis Heuristic algorithms whales whale optimization algorithms Semiconductor diodes

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Multi-Objective Task Scheduling in the Cloud Environment Using Reinforcement Learning and whale optimization Algorithm

Multi-Objective Task Scheduling in the Cloud Environment Usi...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： V. Mahalakshmi V. Poornima Department of Computer Science Vel's Institute of Science Technology and Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

Cloud Computing (CC) enables organizations and individuals to alter and revolutionize the procedure by which people obtain and utilize resources. Due to the growth of the CC, the complexity increases and the cloud systems demand an effective solution for optimal resource allocation. However, effective utilization of resources in CC systems remains a significant problem due to their scale, heterogeneity, and dynamic nature. Artificial Intelligence (AI) has grown up as an effective means to improve management of resources effectively. Task scheduling is one of the major aspects in resource allocation and this study focuses on dynamic task scheduling algorithms to effectively allocate the task to the appropriate Virtual Machines (VMs). This study examines and suggests the reinforcement learning approach with a whale optimization algorithm (RL-WOA) for efficient task allocation in the cloud environment and its performance in terms of efficiency measures in terms of QoS metrics, makespan, CPU utilization and memory usage. The implementation performed using a CloudSim simulator for demonstrating the efficiency of the suggested model.

关键词： Cloud computing Heuristic algorithms Decision making Reinforcement learning Dynamic scheduling whale optimization algorithms Virtual machines Resource management Artificial intelligence optimization

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A Resource Allocation Algorithm for 5G Electric Power IoT Communication Systems Based on a Multi-Strategy Collaborative Improved whale optimization Algorithm

A Resource Allocation Algorithm for 5G Electric Power IoT Co...

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Energy, Power and Grid (ICEPG), International Conference on

作者： Jianxue Li Wuzhou Nie Baohao Chen Siliang Suo Miao Liu Jianwei Han Chengliang Zhang China Southern Power Grid EHV Transmission Company Guangzhou Guangdong China Electric Power Research Institute CSG Guangzhou Guangdong China Shenzhen Technology University Shenzhen Guangdong China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

With the rapid development of 5G technology and the swift growth in the number of heterogeneous devices in the Electric Power Internet of Things (EP-IoT), the issue of communication resource allocation in 5G EP-IoT systems has become increasingly important. A reasonable allocation of transmission power for IoT devices can reduce communication interference and improve the overall energy efficiency of EP-IoT systems. This paper proposes a communication resource allocation algorithm based on a Multi-strategy Improved whale optimization Algorithm (MSWOA), which allocates the transmission power of IoT communication terminals under constraint conditions to enhance the total energy efficiency (EE) of the 5G EP-IoT communication system. This strategy introduces multiple improvements to the traditional whale optimization Algorithm, incorporating differential evolution, contrastive learning, and a penalty mechanism. It also adaptively adjusts the scaling factor and crossover probability in differential evolution. Addressing the traditional whale optimization Algorithm’s shortcomings, such as low accuracy in solving complex combinatorial problems, slow convergence speed, and tendency to fall into local optima, our strategy enhances local search capabilities while ensuring global convergence, preventing local optima in the later stages of iterations. Matlab simulation comparisons with several intelligent optimization algorithms verify that our proposed algorithm achieves better results in the communication resource allocation for EP-IoT communication terminals.

关键词： 5G mobile communication Contrastive learning Search problems Physical layer whale optimization algorithms Energy efficiency Power systems Resource management Internet of Things Convergence

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Fast Collision Detection Method Based on whale optimization Algorithm in Virtual Maintenance

Fast Collision Detection Method Based on Whale Optimization ...

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Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Zhiyong Luo Jie Zhang Xin Dong Hao Zuo School of Advanced Manufacturing Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

In response to the problems of slow detection rate and low detection accuracy of traditional collision detection algorithms in complex virtual maintenance environment, a fast collision detection algorithm based on whale algorithm is proposed and applied to virtual maintenance environment. Firstly, the collision detection of objects is carried out by using hierarchical bounding box, and the objects that may collide are screened. Secondly, the collision detection problem of 3D model is transformed into a nonlinear optimization problem of distance between feature pairs in 2D discrete space. Finally, the whale optimization algorithm is used to solve the problem, and the elite-ordinary strategy is introduced, so that the whale individuals can choose the best way to move, so as to improve the detection efficiency of the algorithm. In order to avoid the algorithm falling into the local optimal solution, the mutation step factor is used to enhance the levy flight mechanism, and the adaptive inertia weight is added to make the whale individual dynamically adjust the ability to get rid of the initial position, so as to improve the detection accuracy of the algorithm. The simulation results show that the algorithm has higher collision detection accuracy and less detection time than the commonly used collision detection algorithm after introducing the elite-ordinary strategy.

关键词： Solid modeling Three-dimensional displays Heuristic algorithms whales Transforms whale optimization algorithms Computational efficiency

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Echo State Network with Dung Beetle optimization Algorithm and Its Application

Echo State Network with Dung Beetle Optimization Algorithm a...

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International Conference on Advanced Computer Theory and Engineering, ICACTE

作者： Xiaoyu Chu Pinghui Wu Shurui Fan Kewen Xia School of Electronics and Information Engineering Hebei University of Technology Tianjin China

ISBN: (数字)9798331539887

ISBN: (纸本)9798331539894

The echo state network (ESN) has a special hidden layer called the reservoir. Compared with the traditional recurrent neural network (RNN), the ESN has a higher training efficiency, solving the problems of vanishing or exploding gradients. However, there are many parameters in ESN, which can affect the prediction effect. The Dung Beetle optimization (DBO) algorithm shows great advantages in optimization problems and has a good ability to jump out of the local optimum. In this paper, the DBO is used to optimize the parameters of ESN. Optimizing parameters include reservoir neurons, spectral radius, input scale, and sparsity. The DBO-ESN is applied to the classification of breast cancer, and compares with the ESN based on particle swarm optimization (PSO-ESN), the ESN based on whale optimization algorithm (WOA-ESN), and ESN. The results show that the DBO-ESN has the best classification accuracy.

关键词： Training Accuracy Computational modeling Echo state networks Reservoirs Prediction algorithms Breast cancer whale optimization algorithms Classification algorithms optimization

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A Novel Multi-Objective Non-Dominated Sorting Beluga whale optimization Algorithm for WFGD Process

A Novel Multi-Objective Non-Dominated Sorting Beluga Whale O...

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Data Driven Control and Learning Systems (DDCLS)

作者： Junheng Xu Kang Wang Xiaoli Li Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

The operation of coal-fired power plants emits a significant amount of SO 2 , causing a considerable environmental challenge. Wet flue gas desulfurization (WFGD) is the mostly used desulfurization method in current practice for address this issue, however, the simultaneous optimization of desulfurization efficiency and cost remains challenging. Aiming to achieve optimal performance and reduce the computational complexity, this paper proposes a novel multi-objective non-dominated sorting beluga whale optimization (NSBWO) algorithm to concurrently optimize desulfurization efficiency and cost. Derived from the NSGA-II framework, the NSBWO algorithm adopts the beluga whale algorithm to generate offspring instead of the conventional genetic algorithm used in the original algorithm. This modification alleviates the concerns about performance dependence on user-defined parameters, enhancing its performance in multi-objective optimization. Experimental results demonstrate that, compared to classical algorithms, NSBWO exhibits superior overall performance and faster convergence speed. Finally, the algorithm is applied to real-world data sourced from the flue gas desulfurization system of a 1000MW coal-fired power plant unit. The obtained results validate the algorithm's effectiveness in optimizing both the efficiency and cost of the wet flue gas desulfurization system.

关键词： Learning systems Costs whales Control systems whale optimization algorithms optimization Power generation

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Modified whale optimization for Liver Cyst Image Segmentation

Modified Whale Optimization for Liver Cyst Image Segmentatio...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Ramanjot Kaur Baljit Singh Khehra Computer Science & Engineering I.K. Gujral Punjab Technical University Jalandhar Kapurthala India Computer Science & Engineering Baba Banda Singh Bahadur Engineering College Fatehgarh Sahib India

Soft computing is a developing field in the modern era of diagnostic aid. Multilevel thresholding is employed in this study to segment actual liver CT scan images. To segment, an image that is helpful in the detection of a liver cyst, a modified fuzzy c-means clustering (MFCMC) and modified whale optimization algorithm (MWOA) are implemented. At an optimal number of thresholds, 2, 3, 4, and 5, multilevel thresholding is carried out. Results from several optimization algorithms, including Particle Swarm optimization, BAT, Differential Evolution, and clustering algorithm K mean clustering, are compared to the proposed approach’s performance. The MFCMC & MWOA method offers precise visual outcomes in addition to being effective in metric-based Uniformity, Peak signal-to-noise ratio (PSNR) and Root Mean Square (RMS) Error values. Findings indicate that, in contrast to other algorithms, the proposed approach finds the best number of threshold values more rapidly.

关键词： Image segmentation Visualization PSNR Computed tomography Liver Clustering algorithms whale optimization algorithms

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