Part 1 : Introduction to TSP problems and genetic algorithm concepts
1. Introduction to Traveling Salesman Problem (TSP)
The Traveling Salesman Problem (TSP) is one of the classic computer algorithm problems. In this problem, a traveling salesman needs to visit a series of cities and return to the departure city, while ensuring that the total distance traveled is as short as possible and each city is visited only once. The TSP problem is NP-hard, which means there is no known polynomial time solution.
2. Introduction to Genetic Algorithms
Genetic algorithm is an optimization algorithm that simulates natural selection. These algorithms are commonly used to solve optimization and search problems. Its basic principles include selection, crossover (hybridization) and mutation.
- Selection : Selecting the best individuals from a population.
- Crossover : simulates the reproductive process of organisms and combines certain characteristics of two individuals to generate new individuals.
- Mutation : changing certain characteristics of an individual with a certain probability.
3. TSP and genetic algorithm
For the TSP problem, the working principle of the genetic algorithm is as follows:
- Representation : A solution can be represented as a sequential visit sequence of cities.
- Choice : We can evaluate the superiority of a solution by its total distance traveled.
- Crossover : Two sequences can exchange part of their paths to generate a new sequence.
- Mutation : Two cities in the sequence can swap places to simulate a mutation.
4. Introduction to Java implementation
In our Java implementation, I