Explore MATLAB source code curated for "自适应粒子群算法" with clean implementations, documentation, and examples.
Adaptive Particle Swarm Optimization algorithm introduces entropy and average particle distance concepts to standard PSO, significantly improving convergence speed while reducing local optimum entrapment, making it more effective for solving complex optimization problems. Implementation typically involves dynamic inertia weight adjustments and diversity maintenance mechanisms through entropy-based calculations.
The Adaptive Particle Swarm Optimization algorithm improves upon standard PSO by incorporating entropy and average particle distance concepts, significantly accelerating convergence while maintaining global search capabilities. This enhancement reduces susceptibility to local optima and effectively handles complex optimization problems through dynamic parameter adjustments and swarm diversity monitoring.
A MATLAB-implemented application of Adaptive Particle Swarm Optimization algorithm, featuring beginner-friendly code structure with configurable parameters and visual optimization progress tracking
Ready-to-run adaptive PSO algorithm with configurable parameters. Key parameters include: wmax = 0.9; % Maximum velocity constraint wmin = 0.01; % Minimum velocity constraint itmax = 100; % Maximum iteration count c1 = 2; % Learning factors c2 = 2; W = (wmax-wmin)/itmax; % Weight calculation Particle swarm initialization with position and velocity settings xmin = -3; % Parameter minimum bounds xmax = 3; % Parameter maximum bounds N = 100; % Particle population size D = 2; % Number of parameters/dimensions t = 0.01 % Step size control
Adaptive Particle Swarm Optimization Algorithm with MATLAB Implementation
Cloud Model-based Adaptive Particle Swarm Optimization (CM-APSO) integrates cloud model theory to enhance traditional PSO's adaptability and convergence efficiency through dynamic parameter adjustment mechanisms.