Search Results - (( parameter optimization method algorithm ) OR ( parameter adoption case algorithm ))
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Investigation of Meta-heuristics Algorithms in ANN Streamflow Forecasting
Published 2024“…This study investigated the efficacy of a hybrid model that adopted a meta-heuristic algorithm (MHA) as an optimizer to extend the training ANN method, from a gradient-based to a stochastic population-based approach for streamflow forecasting. …”
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Exploration and Exploitation Mechanism in Pairwise Test Case Generation: A Systematic Literature Review
Published 2025“…Covering research from 2014 to 2024, the review evaluates hybrid and metaheuristic strategies, including Pairwise Migrating Birds Optimization-Based Strategies (PMBOS), Pairwise Gravitational Search Algorithm Strategy (PGSAS), Pairwise hybrid Artificial Bee Colony (PhABC), Genetic and Particle Swarm Optimization (GAPSO) algorithm, Hybrid Optimization Algorithm (HOA), and Parameter Free Choice Function based Hyper-Heuristic (PCFHH), among others. …”
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Enhancing reservoir simulation models with genetic algorithm optimized neural networks across diverse climatic zones / Saad Mawlood Saab
Published 2025“…The research improved the predictive models by integrating them with the Genetic Algorithm (GA). The optimizer algorithm (i.e., GA) determines the optimal input variables and internal parameters in the prediction models. …”
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River flow prediction based on improved machine learning method: Cuckoo Search-Artificial Neural Network
Published 2024“…Finding the best value for the hyper-parameters is one of the problems with machine learning algorithms, which have lately been adopted by many academics. …”
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HABCSm: A Hamming Based t -way Strategy based on Hybrid Artificial Bee Colony for Variable Strength Test Sets Generation
Published 2021“…Recently, researchers have been advocating the adoption of meta-heuristic algorithms for t -way testing strategies (where t points the interaction strength among parameters). …”
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HABCSm: A Hamming Based t -way Strategy based on Hybrid Artificial Bee Colony for Variable Strength Test Sets Generation
Published 2021“…Recently, researchers have been advocating the adoption of meta-heuristic algorithms for t -way testing strategies (where t points the interaction strength among parameters). …”
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Development of a multi-objective optimization model for transport and environment in a closed-loop automotive supply chain
Published 2014“…The Multiobjective GSA (MOGSA) algorithm is adopted and then programmed using MATLAB software particularly to the MOACLSC. …”
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Deep continual learning for predicting blast-induced overbreak in tunnel construction / He Biao
Published 2024“…Its ability of continual learning is highly applicable to actual tunnel blasting cases. Third, the integration of metaheuristic algorithms further ascertains the optimal blasting parameters for overbreak minimization under specific rock sections. …”
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Optimal coordination of overcurrent relay protection using evolutionary programming / Noor Shah Rizal Abdul Manap
Published 2018“…Due to the difficulty of existing techniques employing different types of algorithm, the usual optimal coordination of overcurrent relays is generally carried out by heuristic, meta-heuristic optimization methods. …”
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Modified Seird model: a novel system dynamics approach in modelling the spread of Covid-19 in Malaysia during the pre-vaccination period
Published 2023“…This study implemented the preliminary stage of forecasting the COVID-19 data using the proposed SEIRD model and highlighted the importance of parameter optimization. The mathematical model is solved numerically using built-in Python function ‘odeint’ from the Scipy library, which by default uses LSODA algorithm from the Fortran library Odepack that adopts the integration method of non-stiff Adams and stiff Backward Differentiation (BDF) with automatic stiffness detection and switching. …”
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Development of tool life prediction model of TiAlN coated tools during the high speed hard milling of AISI H13 steel
Published 2011“…Öktem et al. [6] incorporated RSM with developed genetic algorithm to optimize cutting parameters for better surface quality in case of Inconel 718. …”
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Book Chapter -
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Model input and structure selection in multivariable dynamic modeling of batch distillation column pilot plant / Ilham Rustam
Published 2015“…Further, after a careful investigation into the OLS algorithm, it was shown that the ERR technique which is an essential part of the algorithm to reach model parsimony, has led the resultant model to select an incorrect model terms albeit some improvement in model selection criteria and validation method adopted in this study. …”
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Modeling of flood water level prediction using NNARX / Fazlina Ahmat Ruslan
Published 2015“…Further, after a careful investigation into the OLS algorithm, it was shown that the ERR technique which is an essential part of the algorithm to reach model parsimony, has led the resultant model to select an incorrect model terms albeit some improvement in model selection criteria and validation method adopted in this study. …”
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Dynamic investment model for the restructed power market in the presence of wind source
Published 2014“…In the third step, the long term optimal investment strategies of the hybrid wind-thermal investor are determined based on the dynamic programming algorithm by considering the long term states of demand growth and fuel price uncertainties. …”
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On Adopting Parameter Free Optimization Algorithms for Combinatorial Interaction Testing
Published 2015“…Addressing this issue, this paper proposes the adoption of parameter free optimization algorithms as the basis of future t-way strategies. …”
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Optimization of turning parameters using genetic algorithm method
Published 2008“…This study about development of optimization for turning parameters based on the Genetic Algorithm (GA). …”
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Undergraduates Project Papers -
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Parameters optimization of surface grinding process with particles swarm optimization, gravitational search, and sine cosine algorithms: a comparative analysis
Published 2018“…The efficiency of the three algorithms are evaluated and compared with previous results obtained by other optimization methods on similar studies. …”
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