Training of Neural Network using Particle Swarm Optimization
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Updated
Jan 16, 2019 - Python
Training of Neural Network using Particle Swarm Optimization
A Julia package for consensus-based optimisation
Binary Particle Swarm Optimization applied to the unit commitment problem in an electric microgrid.
Adaptive Heterogeneous Improved Dynamic Multi-Swarm PSO (A-HIDMS-PSO) Algorithm. Source code for the paper: IEEE SSCI https://ieeexplore.ieee.org/document/9660115
The R package geotopOtim2 is a plugin for the automatic calibration and sensitivity analisis of GEOtop 2.x hydrological model, based on the "Particle Swarm Optimisation" approach and the LHOAT "Latin-Hypercube One-factor-At-a-Time" approach.
A sample project for tasks associated with Evolutionary Algorithms course (Particle Swarm Optimisation or Butterfly Optimisation)
Genetic Algorithm Assisted HIDMS-PSO: A Novel GA-PSO Hybrid Algorithm for Global Optimisation. Source code for the paper: IEEE Congress on Evolutionary Computation (CEC) https://ieeexplore.ieee.org/document/9504852
Practice using Python genetic algorithm/ particle swarm optimization libraries to train a simple multilayer perceptron.
Particle Swarm Optimisation, Genetic Algorithm/Programming for (Gradient-Free) Neural Network Optimisation
Hobbyist Library Combining Machine Learning with Heuristics, Tested Across Supervised and Reinforcement Domains
A plotting package for ConsensusBasedX.jl
A Julia package for consensus-based optimisation
Research on Swarm Intelligence - Ant Colony Optimisation & Particle Swarm Optimisation
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