Search Results - (( rendering optimization based algorithm ) OR ( based optimization _ algorithm ))
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Dynamic Economic Dispatch For Large Scale Power Systems: A Lagrangian Relaxation Approach
Published 1991“…The model considers loading and deloading rates, limits on generators outputs, spinning reserve requirements and group power import-export limits. The solution algorithm is based on Lagrangian relaxation and on exploiting the intimate relationship between optimizing the dual Lagrangian function and Dantzig-Wolfe decomposition. …”
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Using predictive analytics to solve a newsvendor problem / S. Sarifah Radiah Shariff and Hady Hud
Published 2023“…Practical implications In terms of managerial implications, the findings in this research help to frame the adoption of a more advanced analytical approach to forecasting, using a Machine Learning algorithm, in solving a newsvendor problem. The unpredictability of customers’ purchasing patterns postpandemic has rendered heuristic-based forecasting large forecast errors. …”
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Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm
Published 2025“…The safe experimentation dynamics algorithm (SEDA) is one such method that optimizes controller parameters using data-driven techniques. …”
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Data-driven brain emotional learning-based intelligent controller-PID control of MIMO systems based on a modified safe experimentation dynamics algorithm
Published 2025“…The safe experimentation dynamics algorithm (SEDA) is one such method that optimizes controller parameters using data-driven techniques. …”
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Evaluating the Efficacy of Intelligent Methods for Maximum Power Point Tracking in Wind Energy Harvesting Systems
Published 2024“…Furtherly, Artificial Intelligence (AI)-based MPPT algorithms were proposed for the WEHS as either standalone or integrated with the traditional MPPT methods. …”
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Integration of simulation for ergonomics assessment in operation control centre (railway industries) / Adib Zulfadhli Mohd Alias
Published 2019“…This study will focus on the translation of the CAD/Revit model into simulation software, either directly or through the intermediate stage of rendering package. Complete CAD/Revit model can be used to generate simulation model by straight forward translation of the whole model or with the algorithms for optimization. …”
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Development of intelligent evaluation system for product end-of-life selection strategy
Published 2011“…This study integrates the travelling salesman problem with genetic algorithm (TSP-GA) for finding the optimal disassembly sequence and disassembling the EOL product. …”
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Multivariable optimization of carbon nanoparticles synthesized from waste facial tissues by artificial neural networks, new material for downstream quenching of quantum dots
Published 2019“…Based on the results, the model GA-4-7-1 had the most optimal statistical characteristics. …”
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Augmented Reality Framework For Symbian Mobile Devices
Published 2013“…Open source libraries such as OpenGL and OpenCV are used to develop mobile AR framework. Optimized algorithms are used to ensure that AR application running smooth. …”
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Hybrid dynamic scheduling model for flexible manufacturing system with machine availability and new job arrivals
Published 2015“…The idea of hybridizing the newly developed biogeography based optimization algorithm (BBO) with variable neighborhood structure (VNS) is proposed in order to produce a high performance initial schedule in terms of minimum completion time, tardiness and flow time within reasonable amount of time. …”
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Automated fault detection and extraction under gas chimneys using hybrid discontinuity attributes
Published 2021“…Contrary to several available fault tracking techniques that use local seismic continuity like coherency attributes, our hybrid method is based on directed semblance, which incorporates aspects of Dave Haleâ��s superior fault-oriented semblance algorithm. …”
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Intelligent traffic lights using Q-learning
Published 2022“…Q-learning derives benefits from past experiences and determines the optimal course of action based on them. The performance of the proposed system has been measured against that of the traditional system, which is a fixed time cycle system. …”
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Proceeding Paper -
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Cuckoo optimised 2DOF controllers for stabilising the frequency changes in restructured power system with wind-hydro units
Published 2021“…Different types of metaheuristic optimisation methods like teaching–learning-based optimisation (TLBO) and cuckoo search algorithms are suggested to acquire the optimal gain values of the proposed controllers. …”
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Personalized one-shot local adaptation federated learning for mortality prediction in multi-center Intensive Care Unit
Published 2024“…Step 2 enables highly personalized models as students to rebalance global and local data knowledge through knowledge distillation optimizations. Step 3 automatically evolves the best-fitting parameters for the highly personalized model at each center using an adapted genetic algorithm. …”
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Simulated Kalman Filter algorithms for solving optimization problems
Published 2019“…Applications and improvements to the HKA algorithm suggest that optimization algorithm based on estimation principle has a huge potential in solving a wide variety of optimization problems. …”
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Opposition-based Whale Optimization Algorithm
Published 2018“…In order to improve solution accuracy and reliability, this paper proposes a new algorithm based on WOA. The new algorithm called Opposition-based Whale Optimization (OWOA). …”
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Hybrid firefly and particle swarm optimization algorithm for multi-objective optimal power flow with distributed generation
Published 2022“…Finally, a crowding distance and non-dominated-sorting-based multi-objective hybrid firefly & particle swarm optimization (MOHFPSO) algorithm is designed for MOOPF problems. …”
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Rule-Based Multi-State Gravitational Search Algorithm for Discrete Optimization Problem
Published 2015“…In this study, rule-based multi-state gravitational search algorithm (RBMSGSA) algorithm is proposed to solve discrete combinatorial optimization problems. …”
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