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algorithm demand » algorithm sma (Expand Search)
algorithm cl » algorithm _ (Expand Search), algorithm fa (Expand Search), algorithm a (Expand Search)
cl function » cost function (Expand Search)
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Simultaneous analysis of frequency and voltage control of the interconnected hybrid power system in presence of FACTS devices and demand response scheme
Published 2021“…The speculated result of the IHPS is presented and analyzed considering real and reactive power as the function of both voltage and frequency. 9The proposed IHPS under investigation has been mathematically modeled for direct coupling like active power–frequency and reactive power–voltage relationships and cross coupling like active power–voltage and reactive power–frequency relationships. …”
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Accommodating High Penetrations of Renewable Distributed Generation Mix in Smart Grids
Published 2017Get full text
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On-demand deployment of multiple aerial base stations for traffic offloading and network recovery
Published 2019“…We present performance results for the proposed algorithm as a function of various system parameters and demonstrate its effectiveness compared to the close-to-optimal greedy approach and its superiority compared to recent related work from the literature.…”
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Cross entropy error function in neural networks
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A genetic-based algorithm for fuzzy unit commitment model
Published 2000“…The model takes the uncertainties in the forecasted load demand and the spinning reserve constraints in a fuzzy frame. …”
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Economic load dispatch using memetic sine cosine algorithm
Published 2022“…In this paper, the economic load dispatch (ELD) problem which is an important problem in electrical engineering is tackled using a hybrid sine cosine algorithm (SCA) in a form of memetic technique. ELD is tackled by assigning a set of generation units with a minimum fuel costs to generate predefined load demand with accordance to a set of equality and inequality constraints. …”
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Modified Elite Opposition-Based Artificial Hummingbird Algorithm for Designing FOPID Controlled Cruise Control System
Published 2023“…This study proposes a novel approach for designing a fractional order proportional-integral-derivative (FOPID) controller that utilizes a modified elite opposition-based artificial hummingbird algorithm (m-AHA) for optimal parameter tuning. Our approach outperforms existing optimization techniques on benchmark functions, and we demonstrate its effectiveness in controlling cruise control systems with increased flexibility and precision. …”
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A utility-based algorithm for joint uplink/downlink scheduling in wireless cellular networks
Published 2012“…While most existing literature focuses on downlink-only or uplink-only scheduling algorithms, the proposed algorithm aims at ensuring a utility function that jointly captures the quality of service in terms of delay and channel quality on both links. …”
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Comparative analysis of metaheuristic load balancing algorithms for efficient load balancing in cloud computing
Published 2023“…<p dir="ltr">Load balancing is a serious problem in cloud computing that makes it challenging to ensure the proper functioning of services contiguous to the Quality of Service, performance assessment, and compliance to the service contract as demanded from cloud service providers (CSP) to organizations. …”
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Optimized Load-Scheduling Algorithm for CubeSat's Electric Power System Management Considering Communication Link
Published 2023“…An optimization problem is formulated with data rate and BER in the cost function while maintaining energy and power constraints. …”
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A hybrid of clustering and meta-heuristic algorithms to solve a p-mobile hub location–allocation problem with the depreciation cost of hub facilities
Published 2021“…To solve the proposed model, four meta-heuristic algorithms, namely multi-objective particle swarm optimization (MOPSO), a non-dominated sorting genetic algorithm (NSGA-II), a hybrid of k-medoids as a famous clustering algorithm and NSGA-II (KNSGA-II), and a hybrid of K-medoids and MOPSO (KMOPSO) are implemented. …”
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A combined resource allocation framework for PEVs charging stations, renewable energy resources and distributed energy storage systems
Published 2017“…The formulation employs a general objective function that optimizes the total Annual Cost of Energy (ACOE). …”
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On Optimizing Backoff Procedure to Enhance Throughput and Fairness For Wireless LANs
Published 2006“…There is more demand on the services provided by the Wireless devices complying with the IEEE 802.11 standard. …”
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Multi Agent Reinforcement Learning Approach for Autonomous Fleet Management
Published 2019Get full text
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Rate Adaptation in Dynamic Adaptive Video Streaming Over HTTP
Published 2021Get full text
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Energy-Efficient Computation Offloading in Vehicular Edge Cloud Computing
Published 2020“…<p dir="ltr">With the development of electrification, automation, and interconnection of the automobile industry, the demand for vehicular computing has entered an explosive growth era. …”
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Integrated Energy Optimization and Stability Control Using Deep Reinforcement Learning for an All-Wheel-Drive Electric Vehicle
Published 2025“…Furthermore, the reduction in sideslip angle, excellent traction through minimizing tire slip ratio, avoiding oversteering and understeering, and maintaining an acceptable range of energy optimization are demonstrated for DRL controllers, especially for the TD3 and CL TD3 algorithms.</p><h2 dir="ltr">Other Information</h2><p dir="ltr">Published in: IEEE Open Journal of Vehicular Technology<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/ojvt.2025.3606120" target="_blank">https://dx.doi.org/10.1109/ojvt.2025.3606120</a></p>…”
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Energy utilization assessment of a semi-closed greenhouse using data-driven model predictive control
Published 2021“…The proposed method consists of a multilayer perceptron model representing the greenhouse system integrated with an objective function and an optimization algorithm. The multilayer perceptron model is trained using historical data from the greenhouse with solar radiation, outside temperature, humidity difference, fan speed, HVAC control as the input parameters to predict the temperature. …”
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