Search Results - (( using optimization method algorithm ) OR ( storage optimization means algorithm ))

Development of multi-objective optimization methods for integrated scheduling of handling equipment (AGVs, QCs, SP-AS/RS) in automated container terminals by Homayouni, Seyed Mahdi

“…Therefore, two meta-heuristic algorithms, namely genetic algorithm (GA) and simulated annealing (SA) algorithm, were developed to optimize the integrated scheduling of handling equipment. …”
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Optimizing dam and reservoir operations using machine learning by Mohammad Abdullah Adib Almubaidin

Enhancing reservoir operations with charged system search (CSS) algorithm: Accounting for sediment accumulation and multiple scenarios by Almubaidin M.A.A., Ahmed A.N., Malek M.A., Mahmoud M.A., Sherif M., El-Shafie A.

“…Optimization methods vary depending on objectives, reservoir type, and algorithms used. …”

Enhancing reservoir operations with charged system search (CSS) algorithm: Accounting for sediment accumulation and multiple scenarios by Almubaidin, Mohammad Abdullah Abid, Ali, Najah Ahmed *, Marlinda, Abdul Malek, Moamin, A. Mahmoud, Sherif, Mohsen, El-Shafie, Ahmed

“…Optimization methods vary depending on objectives, reservoir type, and algorithms used. …”
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Globalization of Barzilai and Borwein Method for Unconstrained Optimization by Farid, Mahboubeh

“…A review of the minimization methods currently available that can be used to solve unconstrained optimization is also given. …”
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Towards large scale unconstrained optimization by Abu Hassan, Malik

“…The matrix- storage methods can be gread accelerated by means of a simple scaling. …”
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Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm by Reza M.S., Hannan M.A., Mansor M., Ker P.J., Rahman S.A., Jang G., Mahlia T.M.I.

“…The effectiveness of the proposed LSA + LSTM model is assessed using battery aging data from the NASA dataset. In addition, to validate the prediction performance of the proposed LSA + LSTM model, extensive comparisons are performed with other popular optimization-based deep learning methods including artificial bee colony (ABC) based LSTM (ABC + LSTM), gravitational search algorithm (GSA) based LSTM (GSA + LSTM), and particle swarm optimization (PSO) based LSTM (PSO + LSTM) model using different error matrices. …”

Quasi-Newton type method via weak secant equations for unconstrained optimization by Lim, Keat Hee

“…In this thesis, variants of quasi-Newton methods are developed for solving unconstrained optimization problems. …”
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State of charge estimation of lithium-ion batteries in an electric vehicle using hybrid metaheuristic - deep neural networks models by Zuriani, Mustaffa, Mohd Herwan, Sulaiman, Isuwa, Jeremiah

“…This study proposes a novel approach for SoC estimation in BMW EVs by integrating a metaheuristic algorithm with deep neural networks. Specifically, teaching-learning based optimization (TLBO) is employed to optimize the weights and biases of the deep neural networks model, enhancing estimation accuracy. …”
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A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

“…When noise exists in training data, the decision boundary of SVM would deviate from the optimal hyperplane severely. To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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A modified weighted support vector machine (WSVM) to reduce noise data in classification problem by Mohd Dzulkifli, Syarizul Amri

“…When noise exists in training data, the decision boundary of SVM would deviate from the optimal hyperplane severely. To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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New signed-digit {0,1,3}-NAF scalar multiplication algorithm for elliptic curve over binary field by Md Yasin, Sharifah

“…Smaller key size means less storage requirement, low power consumption and computing cost. …”
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A new sinkhole attack detection algorithm for RPL in wireless sensor networks (WSN) by Ali, Mohammed Qasim

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State-of-charge estimation for lithium-ion batteries with optimized self-supervised transformer deep learning model by Dickson Neoh Tze How, Dr.

“…The Transformer model with transferred weights outperformed models trained from scratch using supervised learning. To select the optimal hyperparameters for the Transformer model, the Tree Parzen Estimator(TPE) optimization in combination with the Hyperband pruning algorithm is employed to search for the best combination that yields the lowest Root Mean Squared Error(RMSE)and Mean Absolute Error (MAE) error metrics. …”

Optimal Algorithms for Energy Storage Systems in Microgrid Applications: An Analytical Evaluation Towards Future Directions by Reza M.S., Rahman N., Wali S.B., Hannan M.A., Ker P.J., Rahman S.A., Muttaqi K.M.

Investigating dam reservoir operation optimization using metaheuristic algorithms by Lai V., Essam Y., Huang Y.F., Ahmed A.N., El-Shafie A.

Finite Element and Differential Quadrature Methods for Heat Distribution in Rectangular Fins by Fakir, Md. Moslemuddin

“…Presently there are many numerical solution techniques such as finite element method (FEM), differential quadrature method (DQM), finite difference method (FDM), boundary element method (BEM), Raleigh-Ritz method (RRM), etc. …”
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Wind power forecasting with metaheuristic-based feature selection and neural networks by Mohd Herwan, Sulaiman, Zuriani, Mustaffa, Mohd Mawardi, Saari, Mohammad Fadhil, Abas

“…Specifically, five distinct algorithms - Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Teaching-Learning-Based Optimization (TLBO), and Evolutionary Mating Algorithm (EMA) - are integrated with NN model to identify optimal feature subsets from a comprehensive dataset of 18 diverse features. …”
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Application of genetic algorithm methods to optimize flowshop sequencing problem by Mohd Fadil, Md Sairi

“…Genetic algorithm method was one of the methods that were widely used in solving optimization problem. …”
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Sizing optimization of large-scale grid connected photovoltaic system using dolphin echolocation algorithm / Muhammad Zakyizzuddin Rosselan by Rosselan, Muhammad Zakyizzuddin

“…Later, an Iterative-based Sizing Algorithm (ISA) was developed to determine the optimal sizing solution which was later used as benchmark for sizing algorithms using optimization methods. …”
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