Explore MATLAB source code curated for "蚁群算法" with clean implementations, documentation, and examples.
MATLAB implementation of ant colony optimization algorithm for solving the 76-city Traveling Salesman Problem, achieving near-optimal global solutions suitable for advanced practitioners and researchers.
A simulation program for a new shortest path planning algorithm that utilizes Ant Colony Optimization (ACO) for pathfinding solutions
MATLAB implementation of Ant Colony Optimization (ACO) algorithm for solving the Traveling Salesman Problem (TSP) using the Oliver30 dataset.
Universal ant colony optimization (ACO) program implementation with modular code structure for solving various combinatorial optimization problems
Ant algorithm in MATLAB, also referred to as Ant Colony Optimization (ACO), is primarily used for function optimization and solving optimal value search problems. This bio-inspired algorithm mimics ant foraging behavior to find global optima through probabilistic path selection and pheromone-based coordination mechanisms.
MATLAB implementation of vehicle scheduling optimization using ant colony algorithm with code structure and key function explanations
This program provides the source code implementation for applying ant colony optimization (ACO) to image classification tasks, specifically designed for processing 30x30 pixel images.
UAV Path Planning, Ant Colony Optimization Algorithm, Digital Terrain Modeling
A MATLAB-implemented ant colony algorithm program for shortest path finding, featuring network node coordinates in the package for precise path calculations.
The Ant Colony Algorithm is a probabilistic algorithm designed for finding optimal paths in graphs. It represents a novel general-purpose heuristic approach for solving combinatorial optimization problems, featuring positive feedback mechanisms, distributed computing capabilities, and constructive greedy heuristic search properties. By establishing an appropriate mathematical model, distribution network fault location based on fault overcurrent can be reformulated as a nonlinear global optimization problem. Implementation typically involves defining pheromone matrices, path selection probabilities, and evaporation mechanisms to simulate ant foraging behavior.