Explore MATLAB source code curated for "蚁群算法" with clean implementations, documentation, and examples.
A brief introduction to Ant Colony Optimization algorithms, including standard MATLAB implementations and enhanced algorithm variations with code-level descriptions.
Comprehensive guide to Ant Colony Optimization algorithm with MATLAB source code, designed for beginners. Explores application domains, implementation methodology, and includes executable simulation code with detailed comments. Features practical examples covering path planning, data clustering, and image segmentation applications.
This program implements an ant colony optimization algorithm for solving shortest path problems, which can be readily extended to robot path planning applications with minimal modifications to the core algorithm structure.
This collection features four distinct Ant Colony Algorithm implementations, including three MATLAB versions and one C language version, complete with source code for research and application development.
Ant Colony Optimization algorithm for path planning problems, utilizing iterative search for global optimum with pheromone-based path selection mechanisms
Explore classic algorithm programs including Markov Chain, Monte Carlo methods, Ant Colony Optimization, and more, with practical MATLAB implementations to efficiently solve complex problems.
An effective introductory resource for beginners learning Ant Colony Optimization, with proven working code implementation
MATLAB source code for solving Vehicle Routing Problem (VRP) with 31 cities using Ant Colony Optimization algorithm, featuring parameter configuration, pheromone update mechanisms, and path optimization visualization.
Application Background: Ant colony algorithm was initially applied to solve TSP problems and demonstrated significant advantages due to its distributed nature, strong robustness, and easy integration with other algorithms. However, it also suffers from slow convergence and tendency to fall into local optima. The Traveling Salesperson Problem (TSP), also known as the Chinese Postman Problem, is an NP-hard problem where conventional algorithms struggle to find optimal solutions. Therefore, heuristic algorithms like Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO) are commonly employed. Key Technologies: ACO implementation typically involves pheromone matrix initialization, path construction using probabilistic selection, and pheromone update mechanisms with evaporation rates. Code implementation requires handling both symmetric (distance(i,j)=distance(j,i)) and asymmetric TSP variants through different distance matrix structures.
Source code implementations for various AI algorithms including Ant Colony Optimization, Particle Swarm Optimization, Genetic Algorithms, and Neural Networks with implementation details