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Particle swarm optimization for neural network learning enhancement
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Artificial neural network learning enhancement using Artificial Fish Swarm Algorithm
Published 2011“…Artificial Neural Network (ANN) is a new information processing system with large quantity of highly interconnected neurons or elements processing parallel to solve problems.Recently, evolutionary computation technique, Artificial Fish Swarm Algorithm (AFSA) is chosen to optimize global searching of ANN.In optimization process, each Artificial Fish (AF) represents a neural network with output of fitness value.The AFSA is used in this study to analyze its effectiveness in enhancing Multilayer Perceptron (MLP) learning compared to Particle Swarm Optimization (PSO) and Differential Evolution (DE) for classification problems.The comparative results indeed demonstrate that AFSA show its efficient, effective and stability in MLP learning.…”
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Hybrid of swarm intelligent algorithms in medical applications
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Particle swarm optimization with deep learning for human action recognition
Published 2021“…Since two autoen-coders have been trained separately, the information in the autoencoders is forwarded to the deep learning neural network to recognize human actions. …”
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Machine failure prediction technique using recurrent neural network long short-term memory-particle swarm optimization algorithm
Published 2019“…To optimize between the accuracy and execution time, a population-inspired Particle Swarm Optimization (PSO) algorithm is employed. PSO will be utilized to select the optimal RNN-LSTM topology specifically the learning rate instead of using manual search. …”
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Applying machine learning and particle swarm optimization for predictive modeling and cost optimization in construction project management
Published 2024“…This study emphasizes the importance of Machine Learning and Particle Swarm Optimization (PSO) in the context of predictive modeling and cost optimization within the field of construction project management. …”
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Improved opposition-based particle swarm optimization algorithm for global optimization
Published 2021“…An efficient algorithm must have symmetry of information between participating entities. …”
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PI controller based genetic algorithm for PV inverter using SPWM technique / Muhammad Hafiz Dzikri Mohammad Salihhuddin
Published 2025“…This study focuses on optimizing a three-phase photovoltaic (PV) inverter system using Sinusoidal Pulse Width Modulation (SPWM) with Genetic Algorithm (GA)-optimized Proportional-Integral (PI) controllers. …”
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Bio-inspired for Features Optimization and Malware Detection
Published 2018“…The study compares the bio-inspired algorithm [particle swarm optimization (PSO)] and the evolutionary computation with information gain to find the best features optimization in selecting features. …”
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Bio-inspired for Features Optimization and Malware Detection
Published 2018“…The study compares the bio-inspired algorithm [particle swarm optimization (PSO)] and the evolutionary computation with information gain to find the best features optimization in selecting features. …”
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Multi-step time series prediction using recurrent kernel online sequential extreme learning machine / Liu Zongying
Published 2019“…However, the problems with traditional offline and online learning algorithms in machine learning algorithms are usually faced with parameter dependency, concept drift handling problem, connectionless of neural net and unfixed reservoir. …”
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Enhancing generality of meta-heuristic algorithms through adaptive selection and hybridization
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Feature and Instances Selection for Nearest Neighbor Classification via Cooperative PSO
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Predicting the classification of heart failure patients using optimized machine learning algorithms
Published 2025“…This study proposes an optimized machine learning approach using Gradient Boosting Machine (GBM) and Adaptive Inertia Weight Particle Swarm Optimization (AIWPSO) to predict heart failure survival. …”
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Forecasting of fine particulate matter based on LSTM and optimization algorithm
Published 2024“…Long short-term memory based on metaheuristic algorithms, namely particle swarm optimization and sparrow search algorithm (PSO-LSTM and SSA-LSTM), are first developed and applied to determine the significance input combination to the changes of PM2.5 concentration at respective target stations. …”
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