Explore MATLAB source code curated for "改进粒子群优化算法" with clean implementations, documentation, and examples.
This improved particle swarm optimization algorithm addresses constrained optimization problems, providing valuable reference for applying PSO techniques to constrained scenarios with enhanced implementation strategies and constraint-handling mechanisms.
An improved particle swarm optimization algorithm designed to solve constrained optimization problems, featuring enhanced constraint-handling mechanisms and implementation strategies for better convergence performance.
An improved Particle Swarm Optimization (PSO) algorithm based on hybrid strategies, implemented through two core source files: the main execution program (hpso.m) and parameter configuration module (hPSOoptions.m). The codebase features clean architecture with modular design, facilitating straightforward customization and performance enhancements. Experimental results demonstrate significant improvements over Standard PSO (SPSO) in optimization efficiency.
Implementation of fundamental and improved Particle Swarm Optimization algorithms, including: Basic PSO for unconstrained optimization, Constriction Factor PSO, Linearly Decreasing Weight PSO, Adaptive Weight PSO, Random Weight PSO, Synchronous Learning Factor PSO, Asynchronous Learning Factor PSO, Second Order PSO, Second Order Oscillatory PSO, Chaotic PSO, Selection-based PSO
Program Name: Improved Particle Swarm Optimization Algorithm for Constrained Optimization Problems | Program Function: Solving optimization problems with various constraints | Input Conditions: Various initial conditions and parameter settings | Output Values: Optimal solution position and minimum function value
MATLAB source code for an improved Particle Swarm Optimization (PSO) algorithm featuring adaptive weight adjustment mechanisms, enhancing search efficiency and convergence performance