MATLAB implementation of genetic algorithm to solve TSP problem
Genetic algorithm is an optimization algorithm based on the idea of biological evolution. It searches the solution space of the problem by simulating processes such as natural selection, crossover and mutation. The Traveling Salesman Problem (TSP) is a classic combinatorial optimization problem. The goal is to find a path that allows the traveling salesman to pass through all cities along the way and return to the starting city, with the shortest total path length.
Below we will use MATLAB to implement a genetic algorithm to solve the TSP problem.
First, we need to define the input to the problem. Assuming there are n cities, we can use an n×n distance matrix to represent the distance between cities. Suppose the distance matrix is D, then D(i, j) represents the distance from city i to city j, where the values of i and j range from 1 to n.
Next, we define the parameters of the genetic algorithm. These parameters include population size (populationSize), crossover rate (crossoverRate), mutation rate (mutationRate), number of iterations (maxIterations), etc. You can adjust these parameters to get better results based on your situation.
In each generation of the genetic algorithm, we need to perform selection, crossover and mutation operations. The selection operation determines which individuals will enter the next generation based on their fitness. Common selection methods include roulette selection and tournament selection. The crossover operation creates a new individual by exchanging chromosome segments from two individuals. The mutation operation randomly changes the chromosomes of individuals with a certain probability. The specific implementation of these operations is as follows:
function newPopulation = evolve(population