Search Results - (( based optimization methods algorithm ) OR ( storage optimization means algorithm ))
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Towards enhanced remaining useful life prediction of lithium-ion batteries with uncertainty using optimized deep learning algorithm
Published 2025“…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. …”
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State of charge estimation of lithium-ion batteries in an electric vehicle using hybrid metaheuristic - deep neural networks models
Published 2025“…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
Published 2021“…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
Published 2021“…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
Published 2011“…Efficiently managed point operations can give optimal number of point operations involved throughout the execution of the scalar multiplication algorithm and can reduced cost of the scalar multiplication.…”
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State-of-charge estimation for lithium-ion batteries with optimized self-supervised transformer deep learning model
Published 2023“…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. …”
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Investigating dam reservoir operation optimization using metaheuristic algorithms
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Enhancing reservoir operations with charged system search (CSS) algorithm: Accounting for sediment accumulation and multiple scenarios
Published 2025“…Optimization methods vary depending on objectives, reservoir type, and algorithms used. …”
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Development of multi-objective optimization methods for integrated scheduling of handling equipment (AGVs, QCs, SP-AS/RS) in automated container terminals
Published 2012“…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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Enhancing reservoir operations with charged system search (CSS) algorithm: Accounting for sediment accumulation and multiple scenarios
Published 2024“…Optimization methods vary depending on objectives, reservoir type, and algorithms used. …”
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Wind power forecasting with metaheuristic-based feature selection and neural networks
Published 2024“…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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An improved method using fuzzy system based on hybrid boahs for phishing attack detection
Published 2022“…Moreover, Butterfly Optimization Algorithm and Harmony Search Algorithm were combined as optimization method led to a new method named BOAHS. …”
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Hybrid firefly and particle swarm optimization algorithm for multi-objective optimal power flow with distributed generation
Published 2022“…Therefore, the SMA method is redesigned for optimal location and sizing based on the total active power loss of the systems. …”
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Towards large scale unconstrained optimization
Published 2007“…However for other problems the MF-BFGS method is more competitive because of its rapid convergence. The matrix- storage methods can be gread accelerated by means of a simple scaling. …”
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Sizing optimization of large-scale grid connected photovoltaic system using dolphin echolocation algorithm / Muhammad Zakyizzuddin Rosselan
Published 2018“…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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Opposition-based Whale Optimization Algorithm
Published 2018“…The OWOA use the Opposition-based method to enhance Whale Optimization Algorithm (WOA) performance. …”
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A firefly algorithm based hybrid method for structural topology optimization
Published 2020“…In this paper, a firefly algorithm based hybrid algorithm through retaining global convergence of firefly algorithm and ability to generate connected topologies of optimality criteria (OC) method is proposed as an alternative method to solve stress-based topology optimization problems. …”
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Globalization of Barzilai and Borwein Method for Unconstrained Optimization
Published 2009“…Due to BB method’s simplicity, low storage and numerical efficiency, the Barzilai and Borwein method has received a good deal of attention in the optimization community but despite all these advances, stepsize of BB method is computed by means of simple approximation of Hessian in the form of scalar multiple of identity and especially the BB method is not monotone, and it is not easy to generalize the method to general nonlinear functions. …”
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