and a weighted particle swarm optimization (PSO) algorithm. Trained on real manufacturing data that includes manufacturing variations, the surrogate model better reflects actual fabrication conditions ...

Abstract: The received signal strength (RSS) fingerprint-based technique is extensively utilized for indoor localization, as it does not require time synchronization. However, conventional RSS ...

It’s been difficult to find important questions that quantum computers can answer faster than classical machines, but a new ...

However, our research extends this framework by integrating the predictive model with a particle swarm optimization stage. This second stage identifies the optimal operational parameters that can be ...

This project utilizes Particle Swarm Optimization (PSO) to optimize the parameters of thermal detection systems. The goal is to enhance the detection of thermal patterns, improving the accuracy of ...

This Python module implements hyperparameter optimization using Particle Swarm Optimization (PSO) for various machine learning algorithms in classification task. PSO is a population-based optimization ...

Various advanced optimization methods, including genetic algorithm, particle swarm optimization, artificial bee colony optimization, and teaching-learning-based optimization, are used to determine the ...

In light of this, this paper proposes an optimization mechanism based on a BP neural network surrogate model combined with a multi-objective genetic algorithm. Using the straight beam rib wing box ...

Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, Connecticut 06269, United States Center for Clean Energy Engineering, University of Connecticut, Storrs, ...