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检索条件"主题词=whale optimization algorithms"

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TDOA Indoor Positioning Based on an Improved whale optimization Algorithm

TDOA Indoor Positioning Based on an Improved Whale Optimizat...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Shen Han Liangbo Xie Yanbang Chen School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China International College Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350351019

ISBN: (纸本)9798350351026

In the domain of indoor wireless positioning technology, non-line-of-sight conditions significantly impact signal ranging accuracy, thereby diminishing the positioning accuracy of conventional least squares and CHAN algorithms. To address this issue, we propose an improved whale optimization algorithm (IWOA). Firstly, the target position equation is established based on the time difference of arrival (TDOA) positioning model. Subsequently, we use IWOA to solve the target position equation. Experimental results demonstrate that IWOA enhances positioning accuracy, affirming the algorithm’s effectiveness.

关键词： Location awareness Wireless communication Accuracy Time difference of arrival Simulation Antennas and propagation Mathematical models whale optimization algorithms Distance measurement

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An Optimal Planning of Energy Storage System in Radial Distribution Network Using Aquila optimization Algorithm

An Optimal Planning of Energy Storage System in Radial Distr...

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

作者： Myasar Mundher Adnan A.V.V. Sudhakar S. Arun Kumar G K Madhura S. Kaliappan Department of computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical and Electronics Engineering SR University Warangal Telangana India Department of Electrical and Electronics Engineering Sona College of Technology Salem India Department of Artificial intelligence and Machine learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Mechanical Engineering Amity University Dubai United Arab Emirates

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

Nowadays smart grids are emerging rapidly for their efficiency in operating modern distribution systems. Energy storage systems (ESSs) play a key role in achieving the objectives of smart grids, facilitating the combination of renewable sources and enhancing system reliability. Aquila optimization Algorithm (AOA) is proposed for optimal placement and sizing of ESS within the IEEE Radial Distribution System (RDS) to minimize power losses while maintaining voltage magnitude. This method is used to achieve improvements in loss reduction and voltage profile compared to conventional Analytical Index method and whale optimization Algorithm (WOA). The experimental results indicate that if ESS are sized and installed correctly, the overall losses in the radial system are decreased. The suggested AOA achieved a loss reduction rate of 62.58% and a voltage profile of 79.87 kW in enhancing the performance of distribution systems within smart grid frameworks.

关键词： Renewable energy sources Network topology whale optimization algorithms Smart grids Topology Planning Reliability

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High-performance quarter car active suspension system using PID controller tuned by the dingo optimization algorithm

High-performance quarter car active suspension system using ...

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Power Electronics and Control (SEPOC), Seminar on

作者： Thiago Carvalho Bittencourt Joab Tavares Fagundes Paulo Jefferson Dias de Oliveira Evald Sul-Riograndense Federal Institute Pelotas Brazil Intelligent Systems and Control Group Federal University of Pelotas Pelotas Brazil

ISBN: (数字)9798350379419

ISBN: (纸本)9798350379426

This paper presents a novel approach for tuning a proportional-integral-derivative (PID) controller applied to the active suspension system of an electric vehicle using the dingo optimization algorithm (DOA). The DOA is utilized as the core of a sophisticated controller tuning method and follows systematic rules to provide optimal gains to the PID, enhancing vehicle stability. As an outcome, the security and the comfort of the driver and passengers are increased, once the optimized controller can minimize the vehicle balance on irregular land. The active suspension model is based on a quarter-car representation. Three scenarios were considered in the simulation, including a flat road, a speed bump, and a road with a hole. The results demonstrate that the DOA-tuned PID controller effectively minimizes tracking errors, ensuring stable and precise control of the suspension system without excessive control effort. Another meta-heuristic approach was used for comparison, the whale optimizer algorithm (WOA). The proposed controller presented superior performance, not developing the oscillatory behavior of the WOA-tuned algorithm. Moreover, the settling time using the proposed control approach is null. In this sense, the developed control system enhances the comfort and security of the driver and passengers.

关键词： Suspensions (mechanical systems) Roads Metaheuristics Electric vehicles whale optimization algorithms Stability analysis Security Transient analysis Tuning Vehicles

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Bi-Level Planning for Distributed Power Supply Using the Improved whale optimization Algorithm

Bi-Level Planning for Distributed Power Supply Using the Imp...

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China International Electrical and Energy Conference (CIEEC)

作者： Jinfeng Wang Mengxue Xu School of Electrical and Information Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

Optimizing distributed power supply planning is essential to enhancing the impact of distributed power supply on the secure and cost-effective operation of the distribution network. This study establishes a two-layer programming model and selects wind power, solar, and micro-gas turbines as distributed power sources to be scheduled. This study aims to improve the whale algorithm by utilizing tent mapping, inertial weight, and nonlinear shrinkage factor to address issues like slow convergence and delayed jumping out of local solutions. This study solves the proposed model on the IEEE-33 node system using the enhanced whale algorithm and compares the outcome with other techniques. The enhanced whale algorithm offers the most noticeable improvement in node voltage and the lowest annual comprehensive cost, according to the data.

关键词： Costs Power supplies whales Distribution networks Wind power generation whale optimization algorithms Stability analysis

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Optimal Placement of Distributed Generators and Electric Vehicles Using whale optimization Algorithm (WOA)

Optimal Placement of Distributed Generators and Electric Veh...

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Innovative Sustainable Technologies for Energy, Mechatronics, and Smart Systems (ISTEMS), International Conference on

作者： Rinchen Zangmo Suresh Kumar Sudabattula Sachin Mishra Nagaraju Dharavat School of Electronics and Electrical Engineering Lovely Professional University Punjab India

ISBN: (数字)9798350384246

ISBN: (纸本)9798350384253

Integrating Distributed Generation (DG) sources and Electric Vehicles (EVs) into radial distribution systems (RDS) is a promising approach to enhancing power system efficiency and reliability. This trend is driven by the deregulation of the electric power sector and technical constraints in extending distribution and transmission networks to some areas. By carefully selecting the optimal location and sizing for DG sources and electric vehicles (EVs), it is possible to minimize system losses, improve voltage profiles, and enhance overall reliability. This study suggests the integration of multiple DGs and EV s into the system and how the PLoss get affected along with voltage profiles. However, the PLoss increase if the number of DGs exceeds their optimal level. This study integrates 3 DGs and 3 EVs into the RDS separately. The location of the DGs and EVs are obtained with the help of the voltage stability index, and sizes were obtained using the whale optimization Algorithm (WOA). It is implemented into the IEEE 33 test system to verify its robustness and effectiveness. The results show a loss reduction of 41.96%, 57.52%, and 64.73% for the IEEE bus system with the integration of one, two and three DGs, respectively and a slight increase in power loss concerning EV integration as the system load increases. In addition, the voltage stability index is improved significantly for DGs but was affected in the case of EVs.

关键词： Vehicle-to-grid Electric potential System performance Stability criteria Voltage Power system stability whale optimization algorithms

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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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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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