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检索条件"主题词=Whale optimization algorithms"

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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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A whale optimization Algorithm based Solver for No-wait Flexible Flow Shop Scheduling Problems

A Whale Optimization Algorithm based Solver for No-wait Flex...

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International Conference on Informatics and Computing (ICIC)

作者： Stanley Jonathan Cecilia E. Nugraheni L. Abednego Informatics Dept Parahyangan Catholic University Bandung Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

No-wait Flexible Flow Shop Scheduling (NWFFSSP) is one class of scheduling problems found in production scheduling. An interesting characteristic of NWFFSSP is the requirement that jobs be processed continuously without a break from one particular stage to the next. Good production scheduling considers objectives such as minimizing the total production time (makespan) and the resulting CPU Time. In this paper, NWFFSSP is solved using the whale optimization Algorithm (WOA) metaheuristic algorithm to minimize two objectives, namely the total production time (makespan) and CPU Time. The experimental results conclude that makespan is inversely proportional to the population size and the number of iterations. In contrast, CPU time is directly proportional to the number of whales and iterations. Compared to the other three metaheuristics, namely Genetic Algorithm (GA), Simulated Annealing (SA), and Minimum Deviation Algorithm (MDA), WOA has the best performance in terms of makespan for small-sized problems. While in terms of time, although it does not outperform MDA, WOA is generally better than SA and GA.

关键词： Job shop scheduling Metaheuristics Production Simulated annealing whale optimization algorithms Informatics Genetic algorithms

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Enhanced Short-Term Photovoltaic Power Prediction using a Hybrid Improved whale optimization Algorithm with XGBoost

Enhanced Short-Term Photovoltaic Power Prediction using a Hy...

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Smart Technologies for Power and Renewable Energy (SPECon), International Conference on

作者： Sivakannan Subramani Sathishkumar Hari K Asha V Francis Densil Raj Liliya Ponnu Shaji Dept of Advanced Computing St. Joseph’s University Bengaluru India Dept of Electrical & Electronics Engg Shri Ram Murti Smarak College of Engineering & Technology Bareilly India

ISBN: (数字)9798350383041

ISBN: (纸本)9798350383058

Grid-connected photovoltaic (PV) systems are becoming more and more popular in the renewable energy space, but their volatility and unpredictability present difficulties, especially in the event of variable weather. In order to address these issues, accurate PV power forecasting becomes essential for efficient grid management. In order to address this, we present a model called IMWOA-XGB, which combines XGBoost and the Improved whale optimization Algorithm (IMWOA). With the help of our model, PV power projections should be more accurate, allowing for the creation of well-informed generation plans that reduce the grid effect of PV systems. The root mean square error (RMSE) is 18.867 and the mean absolute error (MAE) is 8.639 for the suggested model.

关键词： Photovoltaic systems Renewable energy sources Weather forecasting Production Predictive models Prediction algorithms whale optimization algorithms

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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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Levy Flight Osprey optimization Algorithm for Task Scheduling in Cloud Computing

Levy Flight Osprey Optimization Algorithm for Task Schedulin...

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

作者： Pradeep Chintale Rajesh Kumar Malviya Narendra Babu Merla Pavana Prasanthi Goli Chinna Gopi Desaboyina Tharun Anand Reddy Sure Enterprise Cloud Platform SEI Investment Company Collegeville USA Cloud Data & AI Practice NTT DATA Plano TX USA Ratings S&P Global Ratings New York USA C&O Individual Contributors USA SEI Public Cloud Engineering SEI Investments PA USA Mobile Applications ServiceNow CA USA

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360677

Task scheduling in a Cloud Computing (CC) environment offers flexible and scalable computing resources that encounter critical challenges that significantly affect the system performance. The existing methods often suffer from minimal convergence rates due to ineffective task scheduling, leading to a degradation in the quality of task scheduling in the CC environment. This paper proposes the Levy Flight Osprey optimization Algorithm (LFOOA) for task scheduling, which provides high-quality scheduling solutions due to its effective search mechanisms. The novel approach aims to improve the convergence rate and reduce the time required for evaluation. The significance of task scheduling in CC lies in its responsibility for assigning tasks to each virtual machine, optimising execution time, and minimizing energy consumption. The existing methods namely, Particle Swarm optimization (PSO), Random Double Adaptive whale optimization Algorithm (RDWOA) and whale optimization Algorithm (WOA) and PS-Grey Wolf optimization (PSGWO) are evaluated in the proposed method. The LDOOA approach achieves better performance with a makespan of 97, resource utilization of 99%, and energy consumption of 1.1.

关键词： Energy consumption Cloud computing Processor scheduling System performance Scheduling whale optimization algorithms Resource management Particle swarm optimization optimization Convergence

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Improved power quality in DFIG based wind energy conversion system using whale optimization technique

Improved power quality in DFIG based wind energy conversion ...

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Recent Innovation in Smart and Sustainable Technology (ICRISST), International Conference on

作者： T. Amar Kiran D. Ravi Kishore K. Charles Uggina Veera Naga Satish Dasari John Wesly Department of Electrical and Electronics Engineering Godavari Institute of Engineering and Technology Rajahmundry India

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

In order for the globe to meet its energy requirements in the future, renewable energy sources (RES) are essential. Many dispersed generations like RESs and non-linear loads are features of the power electronics-based current power system design, which has caused a number of power quality (PQ) issues to arise. Regarding a double fed induction generator (DFIG) driven wind energy conversion system (WECS), this research suggests an optimal control mechanism. In this paper, the benefits of a metaheuristic optimizer, WOA, are applied to determine the optimal tuning of the PI controllers in the study's control system. The sophisticated controllers system is used for the adaptive modification of the discontinuity control gain, reducing the phenomenon of chattering in the stimulation network while maintaining the resilience of the closed-loop system. The PWM rectifier's highest possible voltage is tracked by the PI Controller, which also provides increased dependability. Initially, the DFIG and turbine modeling will be introduced. The rotor dimensions are then changed to achieve vector control over both the reactive and active power when utilizing the suggested whale optimization tuned PI controller. The converter's objectives are to deliver input currents with a reasonable harmonic content in order to run at a power factor of one. Extraction of maximum real power occurs at the interface between the DFIG and the powers electronic converter. When compared to other controlling schemes, the WOA shows highest accuracy of 94.34% and less THD value of 0.98%. Lastly, it's going to use the MATLAB 2021a / Simulink program to do a number of computational simulations in order to verify the suggested controls.

关键词： PI control Accuracy Heuristic algorithms Power quality Metaheuristics Doubly fed induction generators Rotors whale optimization algorithms MATLAB Wind energy conversion

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

Research on selective harmonic elimination technology based ...

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IEEE International Conference on Mechatronics and Automation

作者： Weijie Li Wenyi Zhang College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin China P.R

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

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： Software packages Simulation Bridge circuits Programming Harmonic analysis whale optimization algorithms Inverters

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