Explore MATLAB source code curated for "粒子群优化" with clean implementations, documentation, and examples.
Implementation of maximum between-class variance multi-threshold image segmentation utilizing organizational particle swarm optimization algorithm with detailed code methodology
Implementation of particle swarm optimization for BP neural networks in boiler combustion optimization, featuring detailed code explanations, simulation diagrams, and algorithm workflow. The program uses function-based naming convention - simply rename the main file to match the internal function name and execute.
Implementation of PSO PID particle swarm optimization algorithm for designing optimal traffic flow control systems with enhanced parameter tuning capabilities
Standard Particle Swarm Optimization (PSO) algorithm serves as an introductory program for learning PSO programming implementation
Source includes multi-objective comprehensive learning particle swarm optimization implementation with ZDT test suite and true Pareto fronts for multi-objective optimization. Also contains real Pareto fronts for DTLZ1 and DTLZ2 benchmark problems with accompanying verification data.
This implementation combines Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to create a powerful hybrid optimization approach, particularly effective for solving complex problems requiring global optimal solutions through balanced exploration and exploitation mechanisms.
MATLAB code implementation of Particle Swarm Optimization (PSO) algorithm for wireless sensor network optimization with detailed algorithm explanation and performance evaluation
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.
This repository provides source code implementing particle swarm optimization (PSO) for economic load dispatch problems, featuring complete PSO toolkit integration with algorithmic parameter configuration and fitness function implementation.
A comprehensive collection of programs for particle swarm optimized neural networks, featuring various downloadable implementations from web sources that essentially cover all essential programs and codes required for PSO-based neural network optimization.