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检索条件"主题词=Whale optimization algorithms"

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Analysis and Solution of Grid Configuration optimization Model Based on whale optimization Algorithm

Analysis and Solution of Grid Configuration Optimization Mod...

引用

IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Menglu Han Shichen Ren College of Electronic Information and Automation Tianjin University of Science and Technology Tianjin China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

This paper addresses the optimization of microgrid configurations using a whale optimization Algorithm (WOA). Microgrids, consisting of wind and solar power generation in conjunction with the main grid, supply power to the load. In order to maximize the proportion of renewable energy supplied to the load, a higher proportion of installed wind and solar power generation capacity is required. However, the temporal mismatch between the load and renewable power generation may lead to curtailment issues. Integrating energy storage can alleviate this temporal mismatch, thereby reducing curtailment. Considering the substantial cost of electrochemical energy storage, the investment and returns associated with energy storage deployment are crucial factors. This paper focuses on addressing the economic operation of various microgrids under scenarios without energy storage and with the deployment of $50 \text{kW} / 100 \text{kWh}$ energy storage systems. Initially, a linear programming model is established and solved using the whale optimization Algorithm to obtain optimized energy storage configurations, thereby enhancing the economic viability and resource utilization efficiency of the microgrids.

关键词： Renewable energy sources Microgrids Wind power generation Linear programming whale optimization algorithms Solar power generation Resource management optimization Energy storage Investment

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A Novel Hybrid MPPT Algorithm Based on Super Twisting Sliding Mode and whale optimization Algorithm for PEMFC Systems

A Novel Hybrid MPPT Algorithm Based on Super Twisting Slidin...

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

作者： BADOUD Abd Essalam OULD BOUAMAMA Belkacem KHEMMOUDJ Youcef Aymen KOUADRI Ramzi Département d'Electrotechnique Laboratoire d’ Automatique de Sétif University Ferhat Abbas Sétif 1 Algérie CRIStAL UMR CNRS 9189 Université de Lille Villeneuve d’ Ascq France

ISBN: (数字)9798350349740

ISBN: (纸本)9798350349757

The main objective of this work is to develop an improved approach for monitoring the maximum power point (MPPT) in hydrogen production systems (PEMFC) using super twisting sliding mode based on whale optimization algorithm. The suggested strategy can track the MPP in adverse conditions such as severe temperature and pressure variations. Additionally, this method improves effectiveness and lessens the effect of shattering. The effectiveness of the suggested algorithm is tested under various operational settings and compared to three other algorithms: Grey Wolf Optimizer (GWO), fuzzy logic (FL), and perturbation and observation technique (P&O). The simulation findings demonstrate that the suggested controller for MPPT of PEMFC outperforms previous techniques regarding rapid response, minimal oscillations, and slight overshoot and undershoot.

关键词： Fuzzy logic Electrical engineering Production systems Fluctuations Perturbation methods Hydrogen whale optimization algorithms

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Golden Sine Strategy-Fire Hawk optimization for Energy-Efficient Resource Allocation in IoT-Based Cloud-Fog Environments

Golden Sine Strategy-Fire Hawk Optimization for Energy-Effic...

引用

Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Kalyan C. Gottipati Deepika Maddineni Department of Information Technology Citizens Financial Group Arizona USA Department of Information Technology JPMorgan & Chase Co New York USA

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

The rapid growth of Internet of Things (IoT) has created significant demand for effective resource management in cloud-fog platforms. Therefore, there is a need for efficient resource allocation in the IoT environment to avoid unnecessary delays. In this paper, a novel method named Golden Sine Strategy-Fire Hawk optimization (GSS-FHO) is proposed for resource allocation in IoT-based cloud-fog systems. GSS-FHO minimizes latency, improves resource usage, and decreases energy consumption by optimally allocating resources to end users. It also overcomes challenges of existing metaheuristic optimization algorithms by integrating golden ratio and sine functions, while also enabling balanced exploration-exploitation, thus improving the overall performance and solving the local optimum issues. Additionally, the performance of GSS-FHO is compared with that of the conventional algorithms like Particle Swarm optimization (PSO) and whale optimization Algorithm (WOA) for analysis. The experimental results showcase the effectiveness of GSS-FHO as a reliable solution for dynamic cloud-fog platforms. When compared to existing methods, GSS-FHO reduces the load to 0.003, energy consumption to 0.12, and migration cost to 0.002, thereby demonstrating the proposed method's effective strategy for resource allocation.

关键词： Cloud computing Energy consumption Costs Heuristic algorithms Metaheuristics whale optimization algorithms Resource management Reliability Internet of Things Particle swarm optimization

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whale optimization Algorithm Based on Chaotic Mapping and Dynamic Parameters

Whale Optimization Algorithm Based on Chaotic Mapping and Dy...

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Signal Processing and Intelligent Computing (SPIC), International Conference on

作者： Wei Liu Wenlv Yan Tengteng Ren Tong Li Chuang Zhang College of Information Science and Engineering Shenyang Ligong University Shenyang China Department of Information Science and Engineering Ocean University of China Qingdao China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

To tackle the shortcomings of the original WOA, including its sluggish convergence rate and tendency to approach local optima, an adaptive whale optimization algorithm (MWOA) combining chaotic maps and dynamic parameters is presented. First, a modified tent map was employed to create the whale population, increasing its diversity and distributing it more evenly. Then, to strengthen and balance the capabilities of global and local search, the adaptive weight coefficient is added and the linear convergence factor is modified. Finally, the findings of simulation trials on six test functions demonstrated that MWOA considerably increased convergence accuracy and speed.

关键词： Accuracy Heuristic algorithms Diversity reception Signal processing algorithms whales Signal processing whale optimization algorithms Stability analysis optimization Convergence

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Improved Fireworks optimization Algorithm for Multi-product Human-robot Collaborative Hybrid Disassembly Line Balancing

Improved Fireworks Optimization Algorithm for Multi-product ...

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IEEE International Conference on Human-Machine Systems

作者： Feng Guo Xiwang Guo Jiacun Wang Weitian Wang Jinrui Cao Imane Bigaume Information and Control Engineering Liaoning Petrochemical University Fushun China Computer Science and Software Eng. Monmouth University Wes Long Branch New Jersey USA School of Computing Montclair State University Montclair USA Computer Science Department New Jersey City University Jersey City USA

ISBN: (数字)9798350315790

ISBN: (纸本)9798350315806

In the remanufacturing process, the efficient disassembly of end-of-life (EOL) products assumes paramount significance for recycling endeavors. This work introduces a novel disassembly model aiming at enhancing disassembly efficiency within a hybrid disassembly line through the strengths of humanrobot collaboration. In order to optimize the generation of disassembly sequences, this work proposes an improved fireworks optimization algorithm (IFW) that combines uniform mutation and two-point crossover methods. Afterwards, the model’s validity and algorithmic accuracy are substantiated using the CPLEX solver (a commercial solver). Furthermore, a comparative analysis and evaluation against alternative algorithms, such as salp swarm algorithm (SSA) and whale optimization algorithm (WOA), demonstrate a better stability of the proposed algorithm in the human-robot collaborative hybrid disassembly line balancing problem.

关键词： Accuracy Human-machine systems Collaboration Programming Stability analysis whale optimization algorithms Recycling

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Hybrid Golden Jackal and whale optimization Algorithm (HGJWOA) for Optimizing Resource and Service Allocation in Edge Computing Environments

Hybrid Golden Jackal and Whale Optimization Algorithm (HGJWO...

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Knowledge Engineering and Communication Systems (ICKECS), International Conference on

作者： M Saradha T Sasivanan C Monishkumar J Muralidharan K Nivitha Computer Science and Engineering K. S. Rangasamy College of Technology Tiruchengode India Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

Edge computing has emerged as a promising paradigm to address the latency and bandwidth constraints of traditional cloud computing by bringing computation and storage closer to the data source. In edge computing environments, efficient resource and service allocation are crucial for optimizing performance and meeting user requirements. This paper proposes a novel approach for optimizing resource and service allocation in edge computing environments using a hybrid algorithm (HGJWOA)combining Golden Jackal optimization (GJO) and whale optimization Algorithm (WOA). The hybridization of GJO and WOA aims to leverage the strengths of both algorithms to achieve superior optimization performance. We develop a framework for resource and service allocation, formulate the optimization problem, and define evaluation metrics. The hybrid algorithm is integrated into the framework and evaluated through extensive simulations. Experimental results demonstrate the effectiveness of the proposed approach in improving resource utilization, reducing latency, and enhancing service delivery in edge computing environments compared to traditional optimization algorithms. Additionally, case studies and real-world deployment scenarios illustrate the practical applicability and benefits of the hybrid algorithm. Overall, the proposed hybrid Golden Jackal and whale optimization Algorithm presents a promising solution for optimizing resource and service allocation in edge computing environments, contributing to the advancement of efficient and scalable edge computing systems.

关键词： Energy consumption Computational modeling Soft sensors whales whale optimization algorithms Space exploration Resource management

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Research on Integrated Energy System Planning Based on the whale optimization Algorithm

Research on Integrated Energy System Planning Based on the W...

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

作者： Xu Qian Xiaochen Xu Jingdong Zhu Pengxu Dong Yibo Li Yongli Wang Electromechanical Engineering Institute PowerChina Huadong Engineering Corporation Limited Zhejiang China School of Economics and Management North China Electric Power University Beijing China

ISBN: (数字)9798350377798

ISBN: (纸本)9798350377804

In this paper, a comprehensive energy system planning optimization method based on heuristic algorithm is proposed, which aims to improve the economic efficiency of the system. Firstly, the background and development status of the integrated energy system are analyzed, and the challenges and optimization opportunities of equipment configuration and modeling are discussed. Subsequently, an optimization method based on economic performance was introduced, and the whale optimization algorithm was used for system optimization. Through case analysis, the results show that the proposed method can significantly reduce the operating cost of the system. Under the initial population size of 120 iterations, the whale optimization algorithm converged around the 44th generation, and the final solution obtained was ${\$}$6,218,367.29. The results obtained by the genetic algorithm are $1.90 \%$ higher than those obtained using the whale optimization algorithm. Therefore, the algorithm used in this paper shows strong global search ability and improves the optimization accuracy.

关键词： Analytical models Adaptation models Uncertainty Heuristic algorithms optimization methods Dynamic scheduling whale optimization algorithms Planning Load modeling Genetic algorithms

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Hybrid Approach Using LSTM Neural Networks and whale optimization Algorithm For Enhancing Depression Diagnosis

Hybrid Approach Using LSTM Neural Networks and Whale Optimiz...

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Future Telecommunications and Artificial Intelligence (IC-FTAI), International Conference on

作者： Alwan Atta Dina El Sayad Doaa Ezzat Safaa Amin Mahmoud El Gamal Scientific Computing Department Faculty of Computers and Information Sciences Ain Shams University & Galala University Scientific Computing Department Faculty of Computers and Information Sciences Ain Shams University

ISBN: (数字)9798331534080

ISBN: (纸本)9798331534097

Depression is a prevalent mental disorder, and accurate diagnosis is essential for effective intervention by proposing a novel approach for diagnosing depression using Long Short-Term Memory (LSTM) neural networks combined with swarm intelligence algorithms. Accordingly, this research pays major attention to how the strength of LSTM in sequential data and optimization performance through swarm intelligence enhance the accuracy and reliability of the diagnosis of depression. So far, we utilized the whale optimization Algorithm (WOA) algorithm for the optimal tuning of the model parameters of LSTM toward their optimal performance. The experimental results confirm the effectiveness of our approach, where the proposed model has an RMSE value of 3.75 and an MAE value of 3.15. These results indicate a higher performance compared to state-of-the-art methods and offer great potential for LSTMs combined with the WOA algorithm in the accurate diagnosis of depression. This may be a signal that the hybrid approach will serve perfectly in a clinical setting by allowing clinicians to identify depression among patients more precisely and reliably.

关键词： Accuracy Neural networks Depression whale optimization algorithms Telecommunications Telecommunication network reliability Particle swarm optimization Long short term memory Tuning optimization

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Enhancing Arrhythmia Disease Classification Using MLP with whale optimization Approach

Enhancing Arrhythmia Disease Classification Using MLP with W...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： R. Mythili A.S. Aneetha Department of Computer Science School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Chennai Tamil Nadu India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Cardiac arrhythmias are a serious health danger. Thus, timely diagnosis and treatment depend on precise and effective categorization techniques. The paper addresses the implementation of Multilayer Perceptron (MLP) in conjunction with whale optimization-based feature selection, Particle Swarm optimization (PSO), and Antlion Optimizer (ALO) for the categorization of Atrial Fibrillation (AFib) illnesses. Key performance indicators, including Accuracy, precision, and recall, will be examined to determine whether MLP using whale optimization for feature selection works better than conventional techniques. The results indicate that the suggested strategy dramatically improves the classification performance compared to established methods based on thorough experimentation and comparative analysis. The combination of MLP and whale optimization-based feature selection exhibits excellent recall, Accuracy, and precision and is a viable option for reliable AFib illness categorization. From the results obtained, the proposed MLP+WO gave an Accuracy of 90.74 % and precision and recall of 0.90 and 0.88, respectively. The language used is Python.

关键词： Accuracy Arrhythmia Neural networks optimization methods Multilayer perceptrons Feature extraction whale optimization algorithms

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Stochastic optimization Approach for Energy Hub Design and Operation Incorporating Hydrogen Storage, Electric Vehicles, and Demand Response

Stochastic Optimization Approach for Energy Hub Design and O...

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International Middle-East Power System Conference, MEPCON

作者： Ghada Abdulnasser Abdelfatah Ali Mostafa F. Shaaban Essam E. M. Mohamed Department of Electrical Engineering South Valley University Qena Egypt Department of Electrical Engineering American University of Sharjah Sharjah UAE

ISBN: (数字)9798350379648

ISBN: (纸本)9798350379655

The integration of hydrogen storage systems (HSSs) and electric vehicles (EVs) into smart energy hubs (EHs) presents significant opportunities for enhancing energy efficiency, reliability, and clean operation. Accordingly, this study presents a comprehensive approach for the optimal design and efficient operation of smart EH systems equipped with solar-powered compressed air energy storage (SPCAES), HSS, battery storage systems, and thermal energy storage (TES). Moreover, multiple EV parking lots have been considered in the proposed approach. Additionally, the proposed model has also employed the demand response program (DRP), which plays a crucial role in optimizing the balance between energy supply and demand. The key objectives to be minimized include operational costs and greenhouse gas emissions. The proposed approach is tested on the IEEE-33 bus distribution system. Overall, the findings underscore the importance of coordinated management of diverse energy resources to achieve optimal performance in smart energy systems, this is demonstrated by the reductions of 4.16% in investment costs, 23.76% in operational costs, and 17.96% in emissions costs.

关键词： Vehicle-to-grid Costs Hydrogen storage Supply and demand Hydrogen Stochastic processes Demand response whale optimization algorithms Thermal energy Investment

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