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Modelling of optimal placement and sizing of battery energy storage system using hybrid whale optimization algorithm and artificial immune system for total system losses reduct...
Published 2023“…Lastly, the effectiveness of WOA and WOA-AIS in attaining optimal solutions was validated with other well-known optimisation algorithms, including particle swarm optimisation (PSO) and firefly algorithm (FA). …”
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A modified flower pollination algorithm and carnivorous plant algorithm for solving engineering optimization problem
Published 2021“…Flower pollination algorithm (FPA) is a biomimicry optimisation algorithm inspired by natural pollination. …”
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Optimisation of fed-batch fermentation process using deep reinforcement learning
Published 2023“…The proposed deep reinforcement learning algorithm, which integrates an artificial neural network with traditional reinforcement learning, was formulated based on the optimisation objective by manipulating only the substrate feeding rate. …”
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Optimized Cover Selection for Audio Steganography Using Multi-Objective Evolutionary Algorithm
Published 2023“…Therefore, this research proposed a new method for optimising cover audio selection for audio steganography using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), which falls under the MOEA Pareto dominance paradigm. …”
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Multi-Objective Optimal Energy Management of Nanogrid Using Improved Pelican Optimization Algorithm
Published 2025“…In order to demonstrate the effectiveness of the proposed algorithm, The outcomes of the simulation are juxtaposed with results obtained from the initial Pelican Optimisation Algorithm (POA), the Bat Algorithm, and the Improved Differential Evolutionary (IDE) Algorithm. …”
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An enhanced support vector regression -African Buffalo optimisation algorithm for electricity time series forecasting
Published 2023“…This study proposes a hybrid approach combining SVR and the African Buffalo Optimization (ABO) algorithm. The classical ABO algorithm faces limitations in population initialization, exploration, and exploitation. …”
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Hybrid evolutionarybarnacles mating optimisation-artificial neural network based technique for solving economic power dispatch planning and operation / Nor Laili Ismail
Published 2024“…In this study, a new optimisation algorithm termed Hybrid Evolutionary-Barnacles Mating Optimisation (HEBMO) was initially formulated to solve optimisation problems. …”
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Using genetic algorithms to optimise land use suitability
Published 2012“…Second task is to determine the fitness function for the genetic algorithms. The third objective is to optimize the land use map using economic benefits. …”
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Investigating The Performance Of Metaheuristics To Optimize The Revenue Of Semiconductor Supply Chain
Published 2022“…Investigating the performance of metaheuristics to optimise the revenue of the semiconductor supply chain is the final year project that focuses on to identify the supply chain shortage problem related to semiconductors and to compare among the 20 metaheuristics algorithm optimization programmes capable of optimising the semiconductor supply chain. …”
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Gravitational Search Algorithm Based LSTM Deep Neural Network for Battery Capacity and Remaining Useful Life Prediction With Uncertainty
Published 2025“…Moreover, the LSTM model hyperparameters are optimized using the GSA optimization technique. To evaluate the robustness of the proposed method, 15 prediction samples are generated to calculate the uncertainty levels (95% CI) of the predicted RUL. …”
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Meta-heuristic approaches for reservoir optimisation operation and investigation of climate change impact at Klang gate dam
Published 2023“…The Whale Optimisation Algorithm (WOA), Harris Hawks Optimisation (HHO) Algorithm, Lévy Flight WOA (LFWOA) and the Opposition-Based Learning of HHO (OBL-HHO) were proposed to simulate the initial model’s response and optimise the Klang Gate Dam (KGD) release operation with observed inflow, water level (storage), release, and evaporation rate (loss). …”
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Evaluating Adan vs. Adam: an analysis of optimizer performance in deep learning
Published 2025“…Based on these results, this paper aims to provide insights into the strengths and limitations of each optimizer, highlighting the importance of considering task-specific requirements when selecting an optimization algorithm for deep learning models.…”
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Proceeding Paper -
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Hybrid optimisation electromagnetic interference shielding effectiveness:mechanical and physical performance of plaster mortar containing palm oil fuel ash using taguchi grey and t...
Published 2019“…Pre-experiment data is optimized based on Taguchi-grey and Flower Pollination Algorithm (FPA) methods. …”
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Leveraging transfer learning and label optimization for enhanced traditional Chinese medicine ner performance
Published 2024“…Additionally, to mitigate the impact of noisy training data, a 10-fold retraining scheme was introduced to optimise the training set. By retraining the model using the optimised training set, an optimal F1 measure of 92.7% was achieved. …”
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A new optimisation framework based on Monte Carlo embedded hybrid variant mean–variance mapping considering uncertainties
Published 2024“…The Monte Carlo Probability Density Function method was initially used to manage uncertainties. The Monte Carlo-embedded MVMO-SH was then used to optimise PVDG in the urban RDN. …”
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Correlation analysis and predictive performance based on KNN and decision tree with augmented reality for nuclear primary cooling process / Ahmad Azhari Mohamad Nor
Published 2024“…Subsequently, predictive models employing k-nearest neighbour and decision tree algorithms are constructed and evaluated based on accuracy, precision, and recall metrics. …”
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Intergrated multi-objective optimisation of assembly sequence planning and assembly line balancing using particle swarm optimisation
Published 2013“…The aim of this research is to establish a methodology and algorithm for integrating ASP and ALB optimisation using Particle Swarm Optimisation. …”
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Optimization Of Pid Controller Using Grey Wolf Optimzer And Dragonfly Algorithm
Published 2018“…In this research, swarming intelligence is used to solve optimisation problem. Grey Wolf Optimizer and Dragonfly Algorithm were chosen. …”
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Monograph -
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Application of the bees algorithm for constrained mechanical design optimisation problem
Published 2019“…Nowadays, many optimisation algorithms have been introduced due to the advancement of technology such as Teaching Learning Based Optimisation (TLBO), Ant Colony Optimisation (ACO), Particle Swarm Optimisation (PSO) and the Bees Algorithm. …”
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