Explore MATLAB source code curated for "pso算法" with clean implementations, documentation, and examples.
This repository contains fundamental MATLAB source code implementing the Particle Swarm Optimization algorithm. While intentionally simplified for educational purposes, the implementation provides a clean foundation for customization and extension to solve various optimization problems.
Implementing PSO algorithm in MATLAB to optimize neuro-fuzzy network controllers, with performance comparison against PID controllers and conventional fuzzy controllers using simulation-based evaluation metrics.
SVM multiclass classifier implementation featuring multi-class classification capabilities enhanced with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for parameter optimization
This MATLAB program implements a Particle Swarm Optimization (PSO) algorithm to solve a 6x6 job shop scheduling problem, featuring Gantt chart visualization. The code demonstrates fundamental PSO operations including particle initialization, velocity updates, and fitness evaluation using makespan minimization. The implementation is well-structured for educational purposes, making it suitable for beginners to understand both scheduling optimization and metaheuristic algorithms.
A globally convergent PSO algorithm designed for intelligent computing development, featuring enhanced optimization capabilities through swarm intelligence principles
The Chaos Particle Swarm Optimization (CPSO) algorithm combines the rapid convergence traits of PSO with the ergodic randomness of chaos optimization. At each generation, CPSO performs additional chaotic searches around the optimal solution identified by PSO to mitigate premature convergence to local optima. This enhancement addresses PSO's limitations, making CPSO a highly efficient optimization method suitable for complex search spaces.
Application Context: The Traveling Salesman Problem (TSP) is a classic NP-hard combinatorial optimization problem where a salesman must visit n cities exactly once and return to the origin while minimizing total travel distance. Particle Swarm Optimization (PSO), introduced by psychologist James Kennedy and electrical engineer Russell Eberhart in 1995, is a global optimization algorithm. This study employs a modified PSO algorithm enhanced with swap operators and swap sequences to solve discrete TSP problems, demonstrating advantages in implementation simplicity, high accuracy, and rapid convergence.
PSOt is a specialized Particle Swarm Optimization toolbox that encapsulates the core PSO algorithm components, exposing adjustable parameters to users. Users simply define their objective function (for minimization or maximization), specify variable boundaries, and configure iteration constraints like maximum velocity (Max_V) to initiate autonomous optimization.
Implementation of a Particle Swarm Optimization (PSO) algorithm for PID parameter optimization, achieving significant performance improvements in control systems
Implementation of PID parameter optimization using Particle Swarm Optimization (PSO) algorithm - This method has been published in a research paper demonstrating excellent control performance and convergence characteristics.