Explore MATLAB source code curated for "粒子群算法" with clean implementations, documentation, and examples.
A comprehensive MATLAB program for particle swarm optimization including neural network integration, featuring intelligent population initialization, adaptive fitness functions, parameter optimization techniques, and multi-objective optimization capabilities.
Implementing image matching through Particle Swarm Optimization algorithm with customizable input images - recommends initial debugging using MATLAB's built-in image datasets for stability verification
Implementation of fractional-order PID controller parameter tuning based on particle swarm optimization algorithm, developed under MATLAB R2007 environment with code structure analysis and optimization methodology
Implementing PSO-optimized BP neural networks for wind power forecasting with practical datasets and case studies, including code implementation insights for parameter optimization and neural network training.
Implementation of Particle Swarm Optimization for system enhancement with fully operational code execution
Standard Particle Swarm Optimization algorithm implemented through six MATLAB (.m) files, ideal for beginners learning PSO concepts and implementation approaches.
Particle Swarm Optimization algorithms have been implemented and tested. Test functions should be added to fitness.m for evaluation, enabling performance analysis through customizable benchmark functions.
A practical implementation combining Simulated Annealing with Particle Swarm Optimization algorithms, featuring fast convergence and robust performance
Application of Particle Swarm Optimization in power grid reactive power optimization, primarily using the basic PSO algorithm combined with Newton's method for power flow calculation components
Particle Swarm Optimization Algorithm and Its Multiple Improved Variants