Explore MATLAB source code curated for "优化算法" with clean implementations, documentation, and examples.
The Shuffled Frog Leaping Algorithm (SFLA) is a novel optimization technique in evolutionary computing, originally introduced by Eusuff and Lansey in 2003 to address combinatorial optimization problems. This population-based metaheuristic mimics frog behavior with memetic evolution and cooperative search strategies.
The particle swarm optimization algorithm based on simulated annealing generates a new position through small-scale random perturbations and calculates fitness values for both old and new positions. Following the Metropolis acceptance criterion of simulated annealing, it accepts the new position as the optimal solution. Experimental results demonstrate that this method significantly improves optimization performance, accelerates convergence speed, and enhances inversion efficiency. Implementation typically involves combining PSO's velocity-position update mechanism with SA's probabilistic acceptance strategy through temperature-controlled probability functions.
Implementation and Analysis of the NSGA-II Optimization Algorithm for Solving Multi-objective Problems
An optimization algorithms package featuring Levenberg-Marquardt (LM) method, Smart-Quart technique, and other advanced optimization approaches with comprehensive code implementation.
Implementation of chaos-based simulated annealing optimization algorithm, a computational optimization technique developed for undergraduate thesis project
MATLAB Image Processing with Simulated Annealing Algorithm for Direct Image Matching and Optimization Techniques
Mutual information-based image registration implemented in MATLAB using Powell's optimization algorithm for efficient multi-dimensional parameter space exploration
Implementation of three PAPR (Peak-to-Average Power Ratio) reduction methods - Clip & Filter, Partial Transmit Sequence (PTS), and Selected Mapping (SLM) - along with their optimization algorithms. Features clean, well-commented code that is executable and highly valuable for research and practical applications.
MATLAB 7.0 code implementation for Particle Swarm Optimization (PSO) algorithm with enhanced computational capabilities and visualization tools
This is source code implementation of the Artificial Bee Colony optimization algorithm, serving as a foundation for custom algorithm development and enhanced understanding of swarm intelligence optimization techniques.