Globally Convergent Stochastic Particle Swarm Optimization Algorithm

MATLAB 2K 203 views 0 downloads 1 credits
Tags:

Resource Overview

A globally convergent stochastic particle swarm optimization algorithm with convergence guarantee. When the optimal particle's solution stagnates, mutation operations are applied to its position and velocity vectors to prevent premature convergence. The algorithm guarantees global convergence given sufficient iterations, with implementation typically involving fitness evaluation checks and adaptive mutation triggers.

Detailed Documentation

This section provides further elaboration on the convergence guarantee mechanism of the globally convergent stochastic particle swarm optimization algorithm. When the optimal particle's solution shows no improvement over successive iterations, the algorithm implements mutation operations on both position and velocity vectors to escape local optima. This approach prevents premature convergence by introducing stochastic perturbations. Code implementation typically involves monitoring fitness improvement trends and applying Gaussian or uniform random mutations when stagnation is detected. Through sufficient iterative computations, the algorithm mathematically guarantees convergence to the global optimum, with key functions including solution quality assessment, stagnation detection, and adaptive mutation parameter control.

You May Also Like