Search Results - (( a distributed learning algorithm ) OR ( parameter optimization based algorithm ))
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Investigation of cross-entropy-based streamflow forecasting through an efficient interpretable automated search process
Published 2024Subjects:Article -
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Multi-Objective Hybrid Algorithm For The Classification Of Imbalanced Datasets
Published 2019“…A new self-adaptive hybrid algorithm (CSCMAES) is introduced for optimization. …”
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An improved optimization algorithm-based prediction approach for the weekly trend of COVID-19 considering the total vaccination in Malaysia: A novel hybrid machine learning approac...
Published 2023“…This study concludes, based on its experimental findings, that hybrid IBMOLSSVM outperforms cross validations, original BMO, ANN and few other hybrid approaches with optimally optimized parameters.…”
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Optimal power flow based on fuzzy linear programming and modified Jaya algorithms
Published 2017“…In the proposed novel QOJaya algorithm, an intelligence strategy, namely, quasi-oppositional based learning (QOBL) is incorporated into the basic Jaya algorithm to enhance its convergence speed and solution optimality. …”
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Software defect prediction framework based on hybrid metaheuristic optimization methods
Published 2015“…The proposed framework and models that are are considered to be the specific research contributions of this thesis are: 1) a comparison framework of classification models for software defect prediction known as CF-SDP, 2) a hybrid genetic algorithm based feature selection and bagging technique for software defect prediction known as GAFS+B, 3) a hybrid particle swarm optimization based feature selection and bagging technique for software defect prediction known as PSOFS+B, and 4) a hybrid genetic algorithm based neural network parameter optimization and bagging technique for software defect prediction, known as NN-GAPO+B. …”
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Hyper-heuristic approaches for data stream-based iIntrusion detection in the Internet of Things
Published 2022“…Therefore, we propose a prominent approach that integrates each of the NN, a meta-heuristic based on an evolutionary genetic algorithm (GA), and a core online-offline clustering (Core). …”
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Parameter estimation in computational systems biology models: a comparative study of initialization methods in global optimization
Published 2022“…Global optimization method based on an enhanced scatter search (ESS) algorithm is a suitable choice to address this issue. …”
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Multi leader particle swarm optimization for optimal placement and sizing of multiple distributed generation for a micro grid
Published 2023“…Thereafter, the Multi-Leader Particle Swarm Optimization algorithm (MLPSO), which is a novel evolutionary optimization technique in the field of power systems was developed and employed in the optimization process. …”
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A Modified Particle Swarm Optimization for Efficient Maximum Power Point Tracking Under Partial Shading Condition
Published 2024“…A modified initialization scheme that leverages grid partitioning and oppositional-based learning is incorporated to produce an evenly distributed initial population across P-V curve. …”
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Machine learning in botda fibre sensor for distributed temperature measurement
Published 2023“…The results obtained in these experiments would provide some overview in deploying machine learning algorithm for characterizing the Brillouin-based fibre sensor signals.…”
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Nomadic people optimizer (NPO) for large-scale optimization problems
Published 2019“…A metaheuristic is defined as an iterative generation process which guides a subordinate heuristic through a combination of different intelligent concepts for exploring and exploiting the solution space; they employ learning strategies to structure information in order to establish efficient near-optimal solutions. …”
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Chaos search in fourire amplitude sensitivity test
Published 2012“…FAST was originally developed based on the Fourier series expansion of a model output and on the assumption that samples of model inputs are uniformly distributed in a high dimensional parameter space.In order to compute sensitivity indices, the parameter space needs to be searched utilizing an appropriate (space-filling) search curve.In FAST, search curves are defined through learning functions, selection of which will heavily affect the global searching capacity and computational efficiency. …”
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Chaos Search in Fourier Amplitude Sensitivity Test
Published 2012“…FAST was originally developed based on the Fourier series expansion of a model output and on the assumption that samples of model inputs are uniformly distributed in a high dimensional parameter space. …”
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Hyper-heuristic framework for sequential semi-supervised classification based on core clustering
Published 2020“…This article presents a prominent framework that integrates each of the NN, a meta-heuristic based on evolutionary genetic algorithm (GA) and a core online-offline clustering (Core). …”
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Energy consumption optimization in the smart homes
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Proceeding Paper -
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Fault classification in smart distribution network using support vector machine
Published 2023“…In this paper, a machine-learning algorithm known as Support Vector Machine (SVM) for fault type classification in distribution system has been developed. …”
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