Search Results - (( model evaluation tool algorithm ) OR ( rate optimization based algorithm ))*
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1
Modeling and multi-objective optimal sizing of standalone photovoltaic system based on evolutionary algorithms
Published 2020“…Moreover, the total force formula is simplified to speed up the exploration for an optimal solution. Six statistical tools are used to show the superiority of the proposed PV model as compared to other models proposed in the literature. …”
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2
Hybrid Cat Swarm Optimization and Simulated Annealing for Dynamic Task Scheduling on Cloud Computing Environment
Published 2018“…In this study, a Cloud Scalable Multi-Objective Cat Swarm Optimization-based Simulated Annealing algorithm is proposed. …”
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3
A comparative study for parameter selection in online auctions
Published 2009“…In this work, three different models of genetic algorithms are considered. In the first model, the crossover and the mutation rate of the genetic algorithms are varied in order to create different combination of crossover and mutation rate. …”
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4
Designing of prediction model for parameter optimization in cnc machining based on artificial neural network / Armansyah ... [et al.]
Published 2025“…These outputs were then utilized to train an ANN prediction model based on a feed-forward backpropagation (FFBP) algorithm. …”
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5
ARTIFICIAL NEURAL NETWORK FOR WATER LEVEL PREDICTION IN A RIVER UNDER TIDAL INFLUENCE
Published 2004“…The back propagation algorithm was adopted for this study. The optimal model found in this study is the network using two hours of antecedent data, with the combination of learning rate and the number of neurons in the hidden layer of 0.8 and 40. …”
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6
Modelling hourly runoff using ann for sg. Sarawak Kanan Basin
Published 2005“…The back propagation algorithm was adopted for this study. With the three months of training length data, the optimal model found in this study is the network using five hours of antecedent data, with the combination of learning rate and the number of neurons in the hidden layer of 0.8 and 150. …”
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7
Intelligent approach for process modelling and optimization on electrical discharge machining of polycrystalline diamond
Published 2018“…The EDM experiment was conducted based on the design experimental matrix. Subsequently, the effectiveness of EDM on shaping PCD with copper tungsten and copper nickel was evaluated in terms of material removal rate (MRR) and electrode wear rate (EWR). …”
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Estimating Missing Precipitation to Optimize Parameters for Prediction of Daily Water Level Using Artificial Neural Network
Published 2006“…The back propagation algorithm was adopted for this study. The optimal model for predicting missing data found in this study is the network with the combination of learning rate and the number of neurons in the hidden layer of 0.2 and 60. …”
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9
Production and characterization of biochar derived from oil palm wastes, and optimization for zinc adsorption
Published 2015“…The incremental back propagation algorithm demonstrated the best results and which has been used as learning algorithm for ANN in combination with Genetic Algorithm in the optimization. …”
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10
Intelligent approach for processmodelling and optimization on electrical dischargemachining of polycrystalline diamond
Published 2020“…The EDM experiment was conducted based on the design experimental matrix. Subsequently, the effectiveness of EDM on shaping PCD with copper tungsten and copper nickel was evaluated in terms of material removal rate (MRR) and electrode wear rate (EWR). …”
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11
A Hybrid Machine Learning and Optimisation-Based Model for Predicting the Success of Business-To-Consumer Software Development Projects in Indonesia
Published 2025“…Building on these findings, a predictive framework is constructed by integrating machine learning algorithms with advanced optimization and data handling strategies. …”
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12
Evaluation of quality attributes and milling metrics of glutinous rice stored under different storage conditions using infrared thermal imaging
Published 2026“…Ten machine learning (ML) algorithms were tested to predict the quality attributes of GR based on the features extracted from TI data. …”
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13
Water level predictio for Limbang basin using multilayer perceptron (mlp) and radial basis function (rbf) neural network
Published 2010“…MLP is trained with conjugate gradient algorithms, trainscg and RBF with newrb. The optimal model found in this study is the MLP which is using four days of antecedent data with combination of learning rate and number of neurons in the hidden layer of 0.6 and 60. …”
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14
Decision support system for optimal design and operation of ponds for watershed runoff management
Published 2010“…The model also works as a framework for science-based decision making tool when formulating landuse policies. …”
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15
Proactive thermal management of photovoltaic systems using nanofluid cooling and advanced machine learning models
Published 2025“…This study highlights the potential of integrating nanofluid-based cooling with data-driven tools in optimizing PV performance for sustainable energy systems.…”
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Hybrid firefly and particle swarm optimization algorithm for multi-objective optimal power flow with distributed generation
Published 2022“…A new meta-heuristic optimization technique called the Slime Mould Algorithm (SMA) approach has a high convergence rate or a few iterations and superior optimization indices analyzed against other algorithms. …”
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17
An enhanced opposition-based firefly algorithm for solving complex optimization problems
Published 2014“…Firefl y algorithm is one of the heuristic optimization algorithms which mainly based on the light intensity and the attractiveness of fi refl y. …”
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Machining optimization using Firefly Algorithm / Farhan Md Jasni
Published 2020“…Based on the previous research on the success of Firefly Algorithm, this approach will be able to optimize the machining parameter of milling operation. …”
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19
Enhancing three variants of harmony search algorithm for continuous optimization problems
Published 2021“…Meta-heuristic algorithms are well-known optimization methods, for solving real-world optimization problems. …”
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PMT : opposition based learning technique for enhancing metaheuristic algorithms performance
Published 2020“…Experimentally, the PMT shows promising results by accelerating the convergence rate against the original algorithms with the same number of fitness evaluations comparing to the original metaheuristic algorithms in benchmark functions and real-world optimization problems.…”
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