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检索条件"主题词=Whale optimization algorithms"

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Using Modified whale optimization Algorithm for Solving Traveling Salesman Problem

Using Modified Whale Optimization Algorithm for Solving Trav...

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Artificial Intelligence of Things (ICAIoT), International Conference on

作者： Ali Abdullah Saleh Mesut Çevik Electrical and Computer Engineering Altinbas University Istanbul Turkey

The main problem of the network is that the wrong designs and incorrect connection lead to major problems such as cost impact problems and network efficiency, the most famous being Fiber Optical network problems that still considered a problem for various types of networking. Travel Salesmen’s Problem (TSP) is one of the most traditional methods for solving this type of problem, depending today on optimization. Modified algorithms may be regarded as one of the most resourceful and effective ways to solve animal-based TSP problems. whale optimization Algorithm (WOA) and Genetic Algorithm (GA) focuses this paper on TSPLIB-type issues. This work showed a greatly promising performance with whale optimization Algorithm and was better than the genetic algorithm.

关键词： Costs Optical fiber networks Traveling salesman problems whale optimization algorithms Internet of Things Artificial intelligence optimization

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Lifetime Enhancement of Heterogeneous WSNs by whale optimization Algorithm

Lifetime Enhancement of Heterogeneous WSNs by Whale Optimiza...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Neeraj Kaushik Amit Singhal Ashish Pandey Dept. of Computer Science and Engineering Monad University Hapur (UP) India Dept. of Electronics and Communication Engineering GCET Greater Noida (UP) India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Wireless sensor networks (WSNs) often trust on batteries for power, which cannot frequently be recharged or replaced easily across several applications. Hence, in this work, a novel approach using, whale optimization Algorithm (WOA) to optimize routing and power constraints, aiming to prolong the battery life of sensor nodes within the network. This purposed technique focuses on energy-efficient clustering and routing strategies to prolong the operational lifespan of individual nodes. Comparative analysis against existing optimization techniques in WSNs proves the superiority of the proposed WOA-based approach, particularly in achieving prolonged lifetimes under diverse power constraints. To authenticate its robustness, the approach is proved across different base station placements and varying populations of sensor nodes and gateways.

关键词： Wireless communication Wireless sensor networks Clustering algorithms Logic gates Routing whale optimization algorithms Robustness Energy efficiency Batteries optimization

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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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River Water Salinity Prediction Utilized a Social Learning and Wavelet Mutation whale optimization Algorithm

River Water Salinity Prediction Utilized a Social Learning a...

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Software, Systems and Information Technology (SSITCON), International Conference on

作者： Kudaravalli Sita Rama Prasad S. Meenakshi Sundaram Merwa Alhadrawi Rana Veer Samara Sihman Brahmatej Rupavath K. Saranya Department of Civil Engineering V. R. Siddharta Engineering College Vijayawada India Department of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computer Science Indiana Wesleyan University Marion Indiana USA Department of Electronics and Communication Engineering Sona college of Technology Salem India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

River water demand forecasting can contribute significantly to water resource management. Analyzing river water salinity prediction involves utilizing datasets on water quality, which can be influenced by various factors, including population growth, economic development, and living standards. However, there are challenges such as susceptibility to falling into local optima, dealing with large input spaces and the increased dimensionality of computational processes, especially with initial clustering input values. To address these challenges, salinity prediction utilizes the Adaptive Neuro Fuzzy Inference System (ANFIS) combined with the Social Learning and Wavelet Mutation whale optimization Algorithm (SMWOA) to estimate water resource demand. The proposed method integrates the SMWOA mutation strategies to enhance linear probability and improve global search capabilities. Results demonstrate that the proposed method ANFIS-SMWOA, achieves superior performance metrics including the Nash-Sutcliffe Efficiency Coefficient (NSE) of 0.996, Root Mean Square Error (RMSE) of $\mathbf{0. 3 2 9 5}$, Coefficient of Determination $R^optimization$ of $\mathbf{0. 9 4 1}$, Normalized RMSE (NRMSE) of $\mathbf{0. 1 2 8,}$, Modified Index of Agreement (md) and Kling-Gupta efficiency (KGE) of 0.983 on water quality datasets. These outcomes confirm the better detection and classification performance of the proposed method compared to other existing methods, such as Relevance Vector Machine (RVM), Improved Grasshopper optimization Algorithm (IGOA), Simulated Annealing (SA), and the Mayfly optimization Algorithm (MOA).

关键词： Fuzzy logic Salinity (geophysical) Software algorithms Water quality Prediction algorithms Inference algorithms whale optimization algorithms Rivers Water resources optimization

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Feature Selection and Parameter optimization of Optimized Extreme Learning Machine for Motor Fault Detection

Feature Selection and Parameter Optimization of Optimized Ex...

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International Conference on CAD Systems in Microelectronics (CADSM)

作者： Dade Wu Hubei Mechanical and Electrical Research and Design Institute Co. Ltd. Wuhan China

In view of the disadvantage of existing motor fault detection algorithms that they cannot adapt to complex and non-linear fault categories and the accuracy is relatively low., we proposed one method for feature selection and parameter optimization based on simulated annealing-based whale optimization algorithm (SAWOA) optimized extreme learning machine (ELM) are proposed. Based on the ELM classification model., SAWOA is used to optimize the selection of network input variables and parameters of hidden layer nodes of ELM. To verify the effectiveness of the proposed method., it is applied to the real motor fault detection example., and compared with the existing methods under the same conditions. The experiment results show that the method achieves good performance in aspect of the classification accuracy and reliability, and show its effectiveness and application potential.

关键词： Solid modeling Extreme learning machines Fault detection Simulation Simulated annealing Feature extraction whale optimization algorithms

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Economic Load Dispatch using African Vulture optimization Algorithm with Consideration of Valve-Point Loading

Economic Load Dispatch using African Vulture Optimization Al...

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International Conference on Advanced Systems and Electric Technologies (IC_ASET)

作者： Kumeshan Reddy Katleho Moloi Department of Electrical Engineering Nelson Mandela University Gqeberha South Africa Department of Electrical Power Engineering Durban University of Technology Durban South Africa

ISBN: (数字)9798350384895

ISBN: (纸本)9798350384901

This work presents the utilization of African Vulture optimization Algorithm (A VOA) to solve the Economic Load Dispatch (ELD) problem. To simulate practical conditions, both transmission losses and the effect of valve-point loading were considered. The algorithm was applied to a 6-bus system, under a 600 MW and 850 MW loading condition. The results were compared to that of other optimization techniques, such as Particle Swarm optimization (PSO), Bat Algorithm (BA), and whale optimization Algorithm (WOA). Results were analyzed in terms of best result, worst result, average value, and standard deviation. Results for the average test for both the 600 MW and 850 MW tests indicated the capability of the A VOA to produce average superiorities of up to 53.98% and 45.26% respectively. The convergence curves of the various tests indicated the capability of the A VOA to produce a superior result in a short space of time and converge quickly.

关键词： Economics Loading Propagation losses Valves whale optimization algorithms Robustness Particle swarm optimization

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Energy-saving optimization of precision air conditioning system in data center based on E-WOA algorithm

Energy-saving optimization of precision air conditioning sys...

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2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Chunyang Jiang Ruping Shao Jing Yang College of Electrical and Control Science Nanjing Tech University Nanjing Jiangsu China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

In China, with the rapid development of information technology, the number of data centers is constantly increasing. Massive data transmission and storage, as well as inefficient precision air conditioning systems, result in significant power consumption in data centers. This article takes data centers as the research object and establishes a model for the energy consumption of various equipment in air conditioning systems based on the working principle of air conditioning. Under the condition of meeting indoor cooling load requirements, taking the total energy consumption of the air conditioner as the objective function and the supply air temperature, chilled water supply temperature, and chilled water return temperature as decision variables, the total energy consumption of precision air conditioning is minimized. The enhanced whale optimization algorithm is used to obtain the optimal solution of parameters that meet the objective function and constraint conditions and optimize the parameters of the precision air conditioning system. The total energy cost of the optimized precision air conditioner has decreased significantly, hourly energy saving is 9.2%, effectively reducing the PUE of the data center.

关键词： Air conditioning Data centers Energy consumption Power demand Linear programming whale optimization algorithms Information technology Environmental management optimization Load modeling

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optimization of Multi-Function Vehicle Bus Scheduling Table Based on Multi-Strategy Hybrid whale optimization Algorithm

Optimization of Multi-Function Vehicle Bus Scheduling Table ...

引用

IEEE Symposium on Intelligent Vehicle

作者： Hu Wu Xinning Li Xianhai Yang School of Mechanical Engineering Shandong University of Technology Zibo China School of Intelligent Manufacturing Zibo Vocational Institute Zibo China

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

In order to improve the load balancing degree, the bandwidth utilization of multi-function vehicle bus (MVB) and the real-time performance of vehicle communication system, an optimization design method of MVB scheduling table based on multi-strategy hybrid whale optimization algorithm (MHWOA) is proposed. The principle of MVB periodic information communication is briefly described. The constraints and optimization objectives of MVB scheduling table are defined. The periodic information scheduling table model is established. The MHWOA is used to optimize the bus scheduling table of multi-function vehicle. The comparison is made with the improved difference algorithm and genetic algorithm. The results show that the MHWOA can obtain more balanced solutions and higher bus utilization in the same search time.

关键词： Reviews Intelligent vehicles Heuristic algorithms Design methodology Dynamic scheduling Load management whale optimization algorithms

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Key Parameters Prediction for Intelligent Control and Energy optimization Based on MIC-BWO-XGBoost

Key Parameters Prediction for Intelligent Control and Energy...

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Cyber-Energy Systems and Intelligent Energy (ICCSIE), International Conference on

作者： Changyao Lv Yaqi Wang Yudong Chen Zhuo Yu Guoliang Hu Hongliang Wu Shuai Li Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Shanghai Aircraft Manufacturing Co. Ltd. Shanghai China College of Information Engineering Shenyang University of Chemical Technology Shenyang China

ISBN: (数字)9798350373318

ISBN: (纸本)9798350373325

In the aviation industry, anodic oxidation serves as a core process for surface modification of aluminum alloy components, albeit accompanied by high energy consumption. Accurate prediction of key parameters can offer decision support for the intelligent control and energy optimization of anodic oxidation. However, the limited sample size resulting from the high cost of data collection and the complex nonlinear patterns caused by the synergy of multiple factors pose a challenge in accurately predicting key parameters. Therefore, we propose a key parameters prediction method based on the Maximum Information Coefficient (MIC), Beluga whale optimization algorithm (BWO), and XGBoost algorithm. Considering the adverse impact of low correlation parameters on prediction accuracy, we first use MIC to extract features from the original data. Then, XGBoost is used to integrate multiple decision trees to ensure the strong fitting ability for nonlinear relationships. Compared to deep learning models, XGBoost performs well even in a limited sample size. In addition, considering XGBoost is prone to overfitting training data as an ensemble learning algorithm, we employ the BWO to optimize four key parameters of the model that are difficult to choose, thereby enhancing model's generalization. The model's performance is evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with values of 0.04388, 0.09410, and 0.20730%, respectively. The experimental results demonstrate that the proposed method outperforms traditional approaches, enabling precise prediction of key parameters and holding significant value for the intelligent control and energy optimization of anode oxidation processes.

关键词： Microwave integrated circuits Accuracy Process control Prediction algorithms Feature extraction Data models whale optimization algorithms Oxidation optimization Intelligent control

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