Explore MATLAB source code curated for "蚁群算法" with clean implementations, documentation, and examples.
MATLAB code implementation for QoS multicast routing using ant colony optimization algorithm in wireless sensor networks - Features mesh network topology support with detailed parameter configuration and path optimization techniques
This MATLAB implementation applies Ant Colony Optimization (ACO) to solve continuous function optimization problems, featuring a grid-strategy approach based on Duan Haibin's "Ant Colony Algorithm: Principles and Applications".
MATLAB-based Ant Colony Optimization Simulation Platform: Algorithm implementation, path visualization, interactive control interface, and performance analysis tools
My personal collection of intelligent algorithms includes over 20 source code implementations covering: Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Differential Evolution (DE), hybrid algorithms like Genetic-Neural Network, PSO-SVM, and PSO-Neural Network, each featuring distinct optimization strategies and parameter tuning approaches.
Intelligent Optimization Algorithms: MATLAB Program for Ant Colony Algorithm - Complete Implementation for Beginner Learning
Neural network improved using ant colony optimization algorithm. ANT_object_func_ant is a neural network objective function generation program that computes fitness values. ANT_ant_new is the main program implementing the complete optimization framework with ant colony-inspired parameter tuning.
Implementation of a hybrid approach combining Ant Colony Optimization (ACO) algorithm with Backpropagation (BP) Neural Network, featuring enhanced optimization through pheromone-based path selection and gradient descent weight adjustments.
MATLAB program implementation of ant colony optimization algorithm for solving continuous function optimization problems, featuring parameter adjustment strategies and performance enhancement techniques.
This comprehensive fuzzy logic toolkit includes complete MATLAB programs with detailed documentation, enabling effective simulations with excellent performance. It covers fuzzy system creation, rule establishment, variable definition, membership function selection, and simulation result analysis. Suitable for both beginners and experienced researchers in fuzzy systems, the package demonstrates practical implementation of ant colony optimization combined with fuzzy logic controllers.
Optimization Computing with Ant Colony Algorithm—Traveling Salesman Problem (TSP) Optimization For detailed tutorial explanations, please refer to the included materials. Due to file size limitations, contact me for high-definition tutorials (1066146635@qq.com).