Explore MATLAB source code curated for "蚁群算法" with clean implementations, documentation, and examples.
Implementation of ant colony algorithm in wireless sensor networks incorporating clustering mechanisms while considering energy consumption factors through pheromone-based path optimization
Coverage Problem in Wireless Sensor Networks Based on Ant Colony Algorithm with Optimization Algorithm Applications
Implementation of neural network optimization using Ant Colony Algorithm. Features ANT_object_func_ant as the neural network-based objective function and Ant_ant_new as the main optimization program with swarm intelligence implementation.
Ant Colony Optimization Algorithm is suitable for finding optimal solutions and achieving shortest path planning with practical implementation approaches.
This implementation provides a complete MATLAB solution for QoS routing optimization using Ant Colony Optimization algorithm. The code includes parameter configuration, pheromone updating mechanisms, and path selection logic to efficiently solve network routing problems with quality-of-service constraints.
The Ant Colony Algorithm is a probabilistic technique for solving combinatorial optimization problems. The core concept adapts behaviors from ant pheromone communication, which serves as an indirect form of environmental modification through stigmergy. In practical implementations, artificial ants communicate by detecting and modifying virtual pheromone trails, with key algorithmic components including pheromone initialization, evaporation mechanisms, and probabilistic path selection.
Introduction to the optimization algorithm employing ant colony methodology for solving specific problems, with demonstrated effectiveness through practical validation and code implementation insights
A comprehensive collection of twelve MATLAB source files implementing the Ant Colony Algorithm, featuring detailed code implementation and algorithmic explanations for optimization problems
MATLAB-based implementation of ant colony algorithm for continuous function optimization, including comprehensive documentation files and research papers with code examples and parameter explanations.
2D path planning implementation based on Ant Colony Algorithm and Dijkstra Algorithm, developed as MATLAB m-files. Simply download and execute the main.m file to run the complete path planning simulation with visualization capabilities.