Search Results - (( variable factors optimization algorithm ) OR ( java application optimisation algorithm ))
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1
Study and Implementation of Data Mining in Urban Gardening
Published 2019“…The system is essentially a three-part development, utilising Android, Java Servlets, and Arduino platforms to create an optimised and automated urban-gardening system. …”
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Variable Global Optimization min-sum (VGOMS) algorithm of decode-and forward-protocol for the relay node in the cooperative channel
Published 2020“…In this study, a straightforward Particle Swarm Optimization approach was used to determine the optimal scaling factor to obtain optimized error corrective performance of the algorithm. …”
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Variable Global Optimization min-sum (VGOMS) algorithm of decodeand-forward protocol for the relay node in the cooperative channel
Published 2019“…In this study, a straightforward Particle Swarm Optimization approach was used to determine the optimal scaling factor to obtain optimized error corrective performance of the algorithm. …”
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Imposed weighting factor optimization method for torque ripple reduction of IM fed by indirect matrix converter with predictive control algorithm
Published 2015“…This paper proposes a weighting factor optimization method in predictive control algorithm for torque ripple reduction in an induction motor fed by an indirect matrix converter (IMC). …”
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Optimization of PID parameters for hydraulic positioning system utilizing variable weight Grey-Taguchi and particle swarm optimization
Published 2014“…This is done by conducting the DOE on the two PSO optimizing parameters: the limit of change in particle velocity and the weight distribution factor. …”
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6
Identification of continuous-time hammerstein model using improved archimedes optimization algorithm
Published 2024“…Consequently, the proposed algorithm reliably determined the most optimal design variables during numerical trials, demonstrating 54.74% mean fitness function and 75.34% variable deviation indices enchantments compared to the traditional AOA. …”
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7
Optimization of PID Parameters Utilizing Variable Weight Grey-Taguchi Method and Particle Swarm Optimization
Published 2017“…This is done by conducting the DOE on the two PSO optimizing parameters: the particle velocity limit and the weight distribution factor. …”
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OPTIMIZATION OF A CRUDE DISTILLATION UNIT USING PARAMETRIC DESIGN OF TAGUCHI AND RESPONSE SURFACE METHODS
Published 2014“…A sequential quadratic programming (SQP) algorithm is then employed to optimize a profit function based onthereduced number of decision variables.…”
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Development of genetic algorithm for optimization of yield models in oil palm production
Published 2018“…Moreover, models were built on the basis of variables that have been selected by the GA. Across the optimization, procedures obtained the best Two Factor Interaction (2FI) models to achieve the best model of oil palm productivity prediction with a value of R2 of 0.948, mean squared error of 0.022, and the model P-value of < 0.0001 in Sabah. …”
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Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli
Published 2018“…This merit is provided by balancing the exploitation of solution structure and exploration of its appropriate weighting factors through use of a robust and efficient optimization algorithm in learning process of GEP approach. …”
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Fuzzy optimization with multi-objective evolutionary algorithms: A case study
Published 2007“…Decision-maker could choose, in a posteriori decision environment, the most convenient optimal solution according to his level of satisfaction and vagueness factor. …”
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Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in �oruh River
Published 2025“…This study combined models such as the artificial neural network (ANN) algorithm with the Firefly algorithm (FA) and Artificial Bee Colony (ABC) optimization techniques for the estimation of monthly SL values in the �oruh River in Northeastern Turkey. …”
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Low Complexity Error Correction in Low Density Parity Check (LDPC) Code Decoder and Encoder for Decode and Forward Cooperative Wireless Communication
Published 2021“…This algorithm only applies the optimization scaling factor at the bit node processing of the variable node operation. …”
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Optimization of Temperature Rise in Turning Using Single Objective Genetic Algorithm
Published 2023“…The parameters involve during this optimization are cutting speed, feed rate, depth of cut and nose radius by using genetic algorithm optimization. …”
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Web-based expert system for material selection of natural fiber- reinforced polymer composites
Published 2015“…Finally, the developed expert system was deployed over the internet with central interactive interface from the server as a web-based application. As Java is platform independent and easy to be deployed in web based application and accessible through the World Wide Web (www), this expert system can be one stop application for materials selection.…”
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Optimal power flow solutions for power system operations using moth-flame optimization algorithm
Published 2021“…In IEEE 30-bus test system, MFO outperform the other optimization methods with 967.589961$/h compared to 971.411400 $/h, 983.738069$/h, 975.346233$/h, 985.198050$/h, 1035.537820$/h for Improved Grey Wolf Optimizer (IGWO), Grey Wolf Optimizer (GWO), Ant Loin Optimizer (ALO), Whale Optimization Algorithm (WOA), and Sine Cosine Algorithm (SCA) respectively. …”
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Non-weighted aggregate evaluation function of multi-objective optimization for knock engine modeling
Published 2017“…This study assumes that a partial and mutual influence between factors is required before such factors can be optimized. …”
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Resource Efficient For Hybrid Fiber-Wireless Communications Links In Access Networks With Multi Response Optimization Algorithm
Published 2021“…This work proposes a Multi response Optimization (MO) algorithm, named MO-LMMHOWAN that apply in Last Mile Mobile Hybrid Optical Wireless Access Network (LMMHOWAN). …”
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Effect of input variables selection on energy demand prediction based on intelligent hybrid neural networks
Published 2015“…The results show that hybrid forecast model provide better performance when it is trained and tested with optimally selected input variable vector (IV), containing historical load and meteorological variables. …”
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