Explore MATLAB source code curated for "TSP问题" with clean implementations, documentation, and examples.
0-1 integer programming has broad applications in assignment problems, knapsack problems, and even TSP problems - all belonging to NP-hard class where exhaustive search becomes infeasible for large-scale instances. This implementation demonstrates a recursive approach to systematically enumerate all possible solutions for verification purposes, with a notable discrepancy found when comparing results against textbook examples.
This MATLAB program provides solutions for the Traveling Salesman Problem (TSP) using various optimization algorithms such as simulated annealing and genetic algorithms. It includes data preparation guidance and implementation details for effective usage.
A comprehensive MATLAB implementation of the basic Ant Colony Optimization algorithm for solving the Traveling Salesman Problem (TSP). This well-commented program features visual result plotting and includes detailed explanations of algorithm principles and execution methods, making it ideal for educational purposes and practical applications.
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.
Solving the TSP Problem with Neural Network Approaches – Implementation Insights and Algorithm Overview
Traditional ant colony algorithms are prone to local optima; this enhanced version introduces a key parameter to prevent premature convergence
Implement a genetic algorithm to solve the Traveling Salesman Problem by inputting parameters following the format specified in TSP1.m. The algorithm utilizes population evolution with customizable genetic operators for optimal route finding.
Implementing genetic algorithms to solve the 50-city TSP problem, ideal for beginners with code implementation insights.
MATLAB implementation of genetic algorithm for solving the Traveling Salesman Problem (TSP), including city coordinates for validation and performance testing.
Implementing particle swarm optimization algorithm to solve the classic traveling salesman problem with code implementation insights