Explore MATLAB source code curated for "微粒群算法" with clean implementations, documentation, and examples.
Implementation of particle swarm optimization to solve an improved 2D maximum entropy algorithm, particularly effective for medical image segmentation with detailed code-level parameter optimization strategies
MATLAB implementation of hybrid prediction model combining Particle Swarm Optimization and Grey System Theory with high accuracy performance
Particle Swarm Optimization (PSO) is an evolutionary computation technique developed by Kennedy and Eberhart in 1995, inspired by simulations of bird flock predatory behavior. Similar to genetic algorithms, PSO operates as an iterative optimization tool but distinguishes itself by leveraging "cooperation" and "competition" among swarm individuals. Particles dynamically adjust their behavior based on personal and collective flight experiences. PSO's key advantage lies in its straightforward implementation with minimal parameter tuning. It has been widely applied to function optimization, neural network training, fuzzy system control, and other domains traditionally addressed by genetic algorithms.
Using Particle Swarm Optimization (PSO) to optimize Support Vector Machine (SVM) parameters -c and -g with implementation insights
A hybrid algorithm combining differential evolution, genetic algorithm, and particle swarm optimization for constrained optimization problems. This implementation successfully obtains optimal solutions for all 13 standard test functions from reference [7] (T.P. Runarsson and X. Yao, "Stochastic ranking for constrained evolutionary optimization," IEEE Trans. Evol. Comput., vol. 4, no. 3, pp. 284-294, Sep. 2000). The algorithm features constraint handling through stochastic ranking and adaptive parameter tuning. For technical inquiries, please visit http://2shi.phphubei.com
MATLAB Implementation of a Particle Swarm Optimization Toolbox with Constriction Factor Enhancement