MATLAB source code and GUI implementation for solving traveling salesman problem based on optimized genetic algorithm
The Traveling Salesman Problem (TSP) is a class of classic combinatorial optimization problems, the goal of which is to find a path that allows the traveling salesman to visit multiple cities in turn and return to the starting point while minimizing the total length of the path. The traveling salesman problem has attracted much attention due to its high complexity and difficulty in solving it, so many optimization algorithms have been proposed to solve this problem. Among them, genetic algorithm has become one of the effective methods to solve the traveling salesman problem because of its powerful global search ability and good robustness.
This article will introduce how to use the improved genetic algorithm to solve the traveling salesman problem, and provide MATLAB source code and graphical user interface (GUI) implementation.
Genetic Algorithm Overview
Genetic Algorithm is an optimization algorithm that simulates the natural evolution process. It searches for the optimal solution to the problem by simulating operations such as genetics, crossover, and mutation. When solving the traveling salesman problem, the basic steps of the genetic algorithm are as follows:
- Initial population: each individual is represented as a city sequence, and a set of initial solutions are randomly generated as the population.
- Evaluate fitness: Evaluate each individual in the current population according to the path length of the cities visited by the traveling salesman as a fitness function.
- Selection operation: Use some selection strategy (such as roulette) to select excellent individuals from the current population for subsequent crossover and mutation operations.
- Crossover operation: Perform crossover operation on the selected individuals to generate new individuals.
- Mutation operation: Perform a mutation operation on the individuals obtained from the crossover to introduce a certain degree of randomness.
- Update the population: add the individuals obtained from the mutation to the current population, and sort them according to their fitness.
- Termination condition check: If the stop condition is met (such as reaching the maximum number of iterations or finding the optimal solution), the algorithm ends; otherwise, return to step 2.