Explore MATLAB source code curated for "蚁群算法" with clean implementations, documentation, and examples.
The Traveling Salesman Problem (TSP) is a critical yet challenging optimization problem frequently encountered in undergraduate and graduate mathematical modeling competitions. I have developed a self-programmed intelligent solution using Ant Colony Optimization (ACO) algorithm, which effectively addresses this problem through simulated ant foraging behavior and pheromone-based path optimization.
Ant Colony Optimization is a probabilistic algorithm for finding optimal paths that mimics ants' path-finding behavior during food search, with implementations involving pheromone updates and path selection mechanisms to solve the classic Traveling Salesman Problem.
Based on an in-depth analysis of particle swarm optimization principles, we developed an enhanced PSO algorithm for TSP: a hybrid particle swarm approach that integrates concepts from genetic algorithms, ant colony optimization, and simulated annealing to solve the Traveling Salesman Problem with improved computational efficiency and solution quality.
Swarm intelligence algorithms encompass ant colony optimization, particle swarm optimization, and artificial immune algorithms, with implementation approaches involving probabilistic path selection, velocity-position updates, and antibody-antigen interaction simulations.
This implementation applies Ant Colony Optimization to mobile robot path planning, achieving rapid and efficient global optimal path solutions through swarm intelligence simulation.
This is a MATLAB-implemented Ant Colony Optimization (ACO) program designed to solve the Traveling Salesman Problem (TSP) with detailed algorithm parameter configurations and path optimization processes.
Implementation of Ant Colony Optimization (ACO) in MATLAB for optimizing 10 distinct functions including unimodal and multimodal functions with single variables, as well as multimodal functions with multiple variables.
This MATLAB source code implements image edge detection based on ant colony optimization algorithm, featuring comprehensive comments for learning and reference purposes. The code demonstrates how artificial ants simulate natural foraging behavior to detect image edges effectively.
Implementation of ant colony algorithm in MATLAB environment for optimizing submersible 3D path planning with excellent performance results
Chaos-Enhanced Ant Colony Optimization Algorithm and Its Application Research in Function Optimization