Explore MATLAB source code curated for "NP难问题" with clean implementations, documentation, and examples.
This program applies POS (Problem Optimization Strategy) to solve NP-hard problems such as the Traveling Salesman Problem (TSP), with practical simulations demonstrating the algorithm's rationality and effectiveness through optimized pathfinding implementations.
Traveling Salesman Problem: The TSP aims to find the shortest possible route that visits each city exactly once from a given set of cities and returns to the origin city. As an NP-hard problem, TSP requires computationally intensive approaches for exact solutions. This project implements a MATLAB-based genetic algorithm that efficiently finds near-optimal solutions through evolutionary operations like crossover and mutation, providing a practical alternative to exhaustive enumeration methods.
The multi-objective goods loading problem represents a complex combinatorial optimization challenge classified as NP-hard. This paper employs a hybrid particle swarm optimization (PSO) algorithm enhanced with genetic algorithm strategies to solve this problem. The hybrid approach integrates crossover and mutation operations from genetic algorithms into the standard PSO framework, preventing premature convergence to local optima while accelerating convergence toward global optimum solutions. Furthermore, the implementation incorporates weight coefficients to balance multiple objectives, ensuring relatively optimal outcomes across all target functions through strategic parameter tuning.
Exploring approaches to tackle NP-hard problems through constructive heuristic algorithms with implementation insights