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检索条件"主题词=Whale optimization algorithms"

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Shape optimization of Magnetorheological Brake for Performance Enhancement

Shape Optimization of Magnetorheological Brake for Performan...

引用

IEEE International Conference on Robotics and Biomimetics

作者： Qiuyu Shi Fajiang Liu Xinyu Wu Fei Gao Southern University of Science and Technology Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Magnetorheological (MR) rotary brakes leverage the magnetically controllable rheological properties of MR fluids to provide damping torque in lower limb assistance devices. This paper utilizes an optimization algorithm (whale optimization Algorithm (WOA)) to optimize the geometric dimension of the MR brake to enhance field-dependent brake torque. First, the mechanical design and optimization process of the MR brake were outlined. Then, the magnetic density of the MR fluid is evaluated using the ANSYS platform. With that, the WOA was exploited to optimize key design parameters across multiple iterations. According to the simulated results, the torque was improved from 17.39 Nm to 25.89 Nm, which increased by 48%. Finally, a prototype with the initial dimension was fabricated and preliminarily tested.

关键词： Stator cores Torque Fluids Shape Magnetomechanical effects Prototypes Magnetic liquids whale optimization algorithms Brakes optimization

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Task Scheduling algorithms for Energy optimization Under Scheduling Duration and Reliability Constraints

Task Scheduling Algorithms for Energy Optimization Under Sch...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Shizhuang Li Bide Hao Shichun Yang Qianlong Duan Wenlong Chen Fan Zhou School of Transportation Science and Engineering Beihang University Beijing China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

High-performance domain controllers' hardware and software are put to the test by the growing array of additional capabilities found in smart, connected cars. In smart connected automobiles, the energy consumption of high-performance domain controllers is contributing to the whole vehicle's energy consumption at an increasing rate, even though hardware systems' computing capacity is also expanding quickly. Thus, it is crucial to research reducing processor energy consumption while maintaining system performance and dependability. This paper investigates the problem of energy-optimized task scheduling in an on-board operating system for SOA-oriented architectures by combining the DVFS technique and the DAG task model under the constraints of scheduling duration and reliability. Firstly, the energy-optimal task scheduling problem for heterogeneous multicore systems is described, focusing on the constraints of scheduling duration and reliability. The shortcomings of traditional task scheduling algorithms are also analyzed. Secondly, a task scheduling algorithm based on the meta-heuristic whale optimization Algorithm (WOA) is proposed. This algorithm includes the design of encoding, decoding, and constraint processing schemes, and assigns processing units and corresponding DVFS levels to each task in the DAG task set to achieve energy consumption optimization while satisfying constraints. To address the suboptimal performance of the whale optimization Algorithm in high-dimensional problems, a multi-strategy optimization approach is introduced. This enhanced algorithm incorporates chaotic mapping, adaptive nonlinear convergence factors, dynamic inertia coefficients, the Lévy flight strategy, and the evolutionary population dynamics strategy. Finally, the effectiveness of the proposed algorithm is validated through simulation experiments.

关键词： Energy consumption Job shop scheduling Scheduling algorithms Heuristic algorithms Operating systems Dynamic scheduling whale optimization algorithms Nonlinear dynamical systems Reliability Vehicle dynamics

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Improved whale optimization Algorithm with Multi-Strategy Fusion

Improved Whale Optimization Algorithm with Multi-Strategy Fu...

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Electronic Information Engineering and Computer Communication (EIECC), International Conference on

作者： Jun Zhang Zibin Wang School of Transportation Science and Engineering Civil Aviation University of China Tianjin China School of Aeronautical Engineering Civil Aviation University of China Tianjin China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

Aiming at the problems of poor initial population diversity, slow convergence speed and easy to fall into local optimization of traditional WOA algorithm, a whale optimization algorithm improved by multi-strategy fusion is proposed. First, the initial population is improved by using Latin hypercubic sampling method to make the initial solution distribution more uniform; then, the local search ability of the algorithm is improved by introducing adaptive weights, and the global search ability of the algorithm is enhanced by combining the Lévy flight strategy; finally, the Cauchy variation is introduced to enhance the ability of the algorithm to jump out of the local optimum. Through optimization experiments on 13 benchmark functions, the results show that the whale algorithm improved by multi-strategy fusion has obvious improvement in both the search accuracy and convergence speed.

关键词： Accuracy Diversity reception Benchmark testing Sampling methods Hypercubes whale optimization algorithms optimization Convergence

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Comparative performance indexs for fractional-order PID controller use of whale optimization algorithm applied to speed DC motor

Comparative performance indexs for fractional-order PID cont...

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

作者： Sofia Mansouri Bachir Bourouba Department of Electrical Engineering Automatic Laboratory of Setif Setif-1 University Algéria

ISBN: (数字)9798350349740

ISBN: (纸本)9798350349757

This paper a compare studying some of renowned performance indices improvement. The objective of this comparison is to identify the best objective function for optimizing the controller parameters using a new métha_heureustic method. optimization was conducted to enhance the performance of fractional PID controllers. In this paper, the (WOA) technique is employed to adjust the parameters of fractional PID controllers. Numerical simulations results with Matlab software are given to selected the good objective function for this class of controller.

关键词： Electrical engineering Software algorithms Linear programming Numerical simulation whale optimization algorithms Software Performance analysis

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optimization of Contactless Distribution in the Post-COVID Era

Optimization of Contactless Distribution in the Post-COVID E...

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IEEE International Conference on Intelligent Transportation Engineering (ICITE)

作者： Yang Zhang Jiacheng Li Lei Li Graduate School of Science and Engineering Hosei University Tokyo Japan Department of Electrical Electronics and Information Engineering Kanagawa University Yokohama Japan

During an emergency, timely and effective distribution of emergency supplies is critical in rescue. In the context of Covid-19, given the difficulties in distributing supplies to communities due to super infectious viruses, unmanned vehicle distribution is studied by taking into account the priority and satisfaction of communities to improve distribution safety and effectiveness of supplies. Furthermore, the influence of distribution time on the overall efficiency is also taken into account, thus ultimately establishing an unmanned distribution model with the shortest distribution time while meeting community satisfaction. The improved whale algorithm is used to solve the dual-objective model and compared with the basic whale optimization algorithm. The results show that the improved whale algorithm demonstrates better convergence, searchability, and stability. The constructed model can scientifically distribute daily necessities to communities while considering their priority and satisfaction.

关键词： COVID-19 whales Transportation Stability analysis whale optimization algorithms Safety Planning

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Different optimization Technique for Ant Cluster Head and Routing Protocol in Wireless Sensor Networks

Different Optimization Technique for Ant Cluster Head and Ro...

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： G. Nithya J. Kartigeyan Towheed Sultana Shashi Kant Dargar Amit Gupta Department of ECE Joginpally B. R. Engineering College Hyderabad India Department of EEE J. B. Institute of Engineering and Technology Hyderabad India Department of ECE J. B. Institute of Engineering and Technology Hyderabad India Department of ECE Kalasalingam Academy of Research and Education Tamilnadu India Department of AI and ML J. B. Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

The whale optimization Algorithm (WOA) is a revolutionary meta-heuristic area dedicated to intelligent issue exploration and optimization. The communal foraging activities of humpback whales inspired this evolutionary optimization strategy. WOA is a population-based search optimization algorithm that mimics the intricate foraging behaviors of humpback whales to give an intelligent approach to looking for the best solutions. This approach has been used to solve a variety of real-world issues, such as numerical optimization, feature selection, and clustering. However, the proposed model routes through CH, with the choice of CH made using the ALWO algorithm based on constraints, namely energy and delay. The suggested E- ALWO method computes the optimal and secure path for the data transfer process based on the fitness measure. WOA provides more exploration capability and is not constrained by the local search of previous approaches, staying indifferent to the initial solution selection. The method outperformed the most common optimization algorithms

关键词： Wireless sensor networks Metaheuristics MIMICs Clustering algorithms Feature extraction whale optimization algorithms Routing protocols Numerical models Sports Genetic algorithms

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Assessment of whale optimization Algorithm in Maximizing Hosting Capacity of Distributed Generators for Distribution Networks

Assessment of Whale Optimization Algorithm in Maximizing Hos...

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International Middle-East Power System Conference, MEPCON

作者： Abdallah Nazih A. A. Abou El-Ela Eman S. Ali Electrical Engineering Department Faculty of Engineering Menoufia University Shebin Elkom Egypt

ISBN: (数字)9798350379648

ISBN: (纸本)9798350379655

Many optimization methods have been utilized to maximize hosting capacity (HC) of distributed generators (DGs) in power networks. In this paper there is an assessment of proposed whale optimization algorithm (WOA) in distribution networks. Genetic algorithm (GA) technique in addition to differential evolution (DE) and ant lion optimization (ALO) algorithms are three well-known optimization methods which are utilized to determine optimal DGs allocations in power distribution networks. These methods performances are compared with the proposed WOA to assess its efficacy in solving the studied problem. The algorithms are applied to both a practical Cairo distribution network as an Egyptian distribution network and a standard IEEE 136-bus system. The study evaluates the effects of each algorithm on the studied networks in terms of DGs allocations, number of units, operational power factors, convergence characteristics, voltage profiles, voltage deviation indices and minimum/maximum voltage values. The results emphasize proposed WOA superiority for optimally maximizing DGs HC with the studied networks compared with other literature techniques.

关键词： Reactive power optimization methods Distribution networks Voltage Generators whale optimization algorithms Resource management Standards Genetic algorithms Convergence

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whale optimization Algorithm Enhanced Multi-Layer Perceptron for Switch Cabinet Fault Diagnosis

Whale Optimization Algorithm Enhanced Multi-Layer Perceptron...

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Artificial Intelligence and Power Systems (AIPS), International Conference on

作者： Quan Shi Wang Ran Ming Fu Dong Fu Wenjie Xu Guangdong Yuedianke Testing Technology Co. Ltd. Guangzhou Guangdong China

ISBN: (数字)9798350392210

ISBN: (纸本)9798350392227

Switch cabinet fault diagnosis, a critical task in power system reliability, has traditionally utilized the Multi-Layer Perceptron (MLP), an approach that occasionally converges to local optima, thereby limiting diagnostic accuracy. The whale optimization Algorithm (WOA), known for its robust optimization capabilities, offers a promising solution to this challenge. This paper introduces a novel methodology that synergistically combines WOA with MLP to enhance switch cabinet fault diagnosis. Initially, the architecture of the MLP neural network is meticulously optimized by determining the optimal number of hidden layers and neurons per layer. Through a systematic evaluation of MLP's recognition performance, a configuration of 3 hidden layers with 12 neurons each is identified and implemented. Subsequently, the study conducts a comprehensive analysis to explore the impact of varying iteration counts and population sizes on the diagnostic accuracy of the integrated WOA-MLP model. This analytical process contributes to the refined precision of switch cabinet fault diagnosis. Finally, the paper employs real-world fault data from a power grid to validate the feasibility and applicability of the WOA-MLP algorithm. Experimental results substantiate the algorithm's efficacy, demonstrating a marked improvement in accuracy and robust stability, thereby affirming its potential as a valuable tool in fault diagnosis.

关键词： Fault diagnosis Accuracy Systematics Robust stability Neurons Switches Power system stability whale optimization algorithms optimization Biological neural networks

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optimization of WSNs Coverage for Power Monitoring Based on Multi-strategy Sand Cat Swarm optimization Algorithm

Optimization of WSNs Coverage for Power Monitoring Based on ...

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International Conference on Information, Communication and Networks (ICICN)

作者： Hongmei Fei Tao Luo Baitao Zhang Zheng Sun Pengwei Ma Jie Zhou College of Information Science and Technology Shihezi University Shihezi China

ISBN: (数字)9798350355802

ISBN: (纸本)9798350355819

With the rapid development of electric power technology, the application of wireless sensor networks (WSNs) in the electric power field has received widespread attention. The WSNs coverage optimization problem in the power field is one of the key factors to improve the power transmission efficiency and resource utilization. However, traditional optimization algorithms have some limitations in dealing with the deployment problem of large-scale sensor nodes. Therefore, this thesis proposes a multi-strategy sand cat swarm optimization algorithm (MSSCSO) to solve the WSNs coverage problem. First, this paper proposes an improved Tent chaos mapping to improve the search range and enhance the local search capability of the algorithm. Secondly, the reverse learning strategy is used to increase the population diversity and global search capability, which improves the convergence speed and optimization accuracy of the algorithm. Finally, the global search capability of the algorithm and the adaptability and robustness of the algorithm are enhanced by the adaptive strategy. In order to evaluate the performance of the proposed method, this paper conducts comparative experiments with Genetic Algorithm (GA), Firefly optimization Algorithm (FA), whale optimization Algorithm (WOA) and Gray Wolf optimization Algorithm (GWO), respectively. The experimental results show that MSSCSO's WSNs coverage optimization method can effectively improve the network coverage in power scenarios by 10.54%, 20.04%, 9.81%, and 6.43% over GA, FA, WOA, and GWO, respectively. Meanwhile, the method has strong adaptability and stability to the uncertainty and dynamic changes in the power environment.

关键词： Wireless sensor networks Uncertainty Heuristic algorithms Stability analysis whale optimization algorithms Robustness Particle swarm optimization optimization Genetic algorithms Convergence

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Fault response strategy for high proportion distributed energy resources distribution area based on whale optimization algorithm

Fault response strategy for high proportion distributed ener...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Hengyu Liu Liyang Liu Xiaoning Ma Dawei Hu Jingwei Han Tianwei Cao Jiazheng Sun Zhi Zhang Electric Power Research Institute of State Grid Liaoning Electric Power Co. Ltd. Shenyang China State Grid Liaoning Electric Power Supply Co. Ltd. Shenyang China The School of Electrical Engineering Shenyang University of Technology Shenyang China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

The proportion of distributed energy resources in the distribution area is increasing, and its output is uncertain. Therefore, considering the uncertainty, a fault recovery strategy combining island division and recovery and reconstruction is proposed. Firstly, a distributed energy resources output model and a load characteristic model are established, and a combination of breadth-first traversal and depth-first traversal is used to divide the islands according to the faults occurring at different time periods. Then, the network reconstruction model is established with the goal of minimum switching operation times and minimum network loss, and the improved whale optimization algorithm is used to solve it, and the residual power loss load in the distribution area is restored. Finally, the simulation analysis of IEEE33 nodes is carried out, and the experimental results show that the proposed method can restore power supply quickly and accurately.

关键词： Wind Uncertainty Power supplies Switches System integration Wind power generation whale optimization algorithms Stability analysis Distributed power generation Load modeling

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