TSP path planning based on genetic algorithm

In recent years, the proposal and development of algorithms have been accelerating, and algorithms with various purposes, principles, simplicity, complexity, and sources have been applied to difficult problems. Whether it is new algorithms or the optimization of existing algorithms, the corresponding algorithm performance is getting better and better, such as genetic algorithm. At the same time, the TSP path planning problem is also a common problem that people encounter in daily life. In order to obtain the optimal value of the path, this paper proposes a TSP path planning method based on genetic algorithm. This method first generates 100 simulated city points; Then, using Python language and following the principles of genetic algorithm and the entire process, data was used as parameter input for 2000 loop iterations. During this process, the total path continuously decreased, and finally a path starting from the starting city point, passing through 100 city points, and finally regressing to a total of 8.4967 starting points was obtained. This simulation test further verifies that genetic algorithm is capable of optimizing TSP path planning, and its performance is also excellent, which can be promoted and used.