Explore MATLAB source code curated for "局部最优" 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.
This MATLAB program implements a hybrid optimization approach combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), featuring significantly improved optimization efficiency and robust avoidance of local optima through integrated evolutionary and swarm intelligence mechanisms.
Registration for 3D Point Cloud Data
Implementation of Bat Algorithm for Parameter Optimization in Backpropagation Neural Networks
Niche Genetic Algorithm Implementation for Solving Multimodal Function Optimization
An enhanced training algorithm for Gaussian Mixture Models implementing improved initialization and momentum-based EM optimization.
MATLAB Code Implementation of Tabu Search Algorithm with Detailed Technical Explanations
The Hill Climbing Algorithm effectively addresses numerous function optimization challenges through iterative local search