Explore MATLAB source code curated for "粒子群算法" with clean implementations, documentation, and examples.
Complete source code for Particle Swarm Optimization algorithm implemented in MATLAB, featuring optimization functions and parameter tuning - ideal for collaborative learning and knowledge exchange
Explains the particle swarm optimization algorithm using a simple illustrative example with implementation insights
Implementation of Particle Swarm Optimization as an enhancement operator for Genetic Algorithms, with code-level integration examples for educational purposes
Application Background: Particle Swarm Optimization (PSO) is an optimization algorithm inspired by natural phenomena such as bird flocking and fish schooling. It simulates particle movement and cooperation within a search space to find optimal solutions. PSO has been widely applied across various fields including engineering optimization, machine learning, and image processing. Its simplicity and efficiency make it a popular choice for solving complex optimization problems. Key Technologies: For each particle i = 1, 2, ...: Initialize particle position with uniformly distributed random vector: x_i ~ U(blo, bup), where blo and bup represent lower and upper search space bounds. Initialize particle's best-known position to its initial position: p_i ← x_i. Update swarm's best-known position if f(p_i) < f(g): g ← p_i. Initialize particle velocity: v_i ~ U(-|bup-blo|, |bup-blo|). The algorithm iteratively updates velocities and positions using social and cognitive components until meeting termination criteria.
Comprehensive introduction to Differential Evolution and Particle Swarm Optimization algorithms with detailed MATLAB code implementations, including key functions and parameter configurations
Using Particle Swarm Optimization to optimize BP neural network weights, the trained neural network is applied to fault diagnosis for pattern recognition, achieving faster convergence compared to standard BP neural networks
Path Planning Using Particle Swarm Optimization Algorithm with Implementation Details
This Particle Swarm Optimization (PSO) toolbox encapsulates the core algorithm components, providing users with adjustable parameters for customization. Users simply need to define their objective function (minimization or maximization), specify variable bounds, set iteration parameters like maximum velocity (Max_V), and the toolbox handles the optimization process automatically with configurable swarm intelligence mechanisms.
Executable source code with customization capabilities for parameter modification and algorithmic enhancements
MATLAB programs implementing two intelligent optimization algorithms (Biogeography-Based Optimization and Particle Swarm Optimization) with detailed code structure explanations and algorithm implementation insights