A TSP problem path planning method

Abstract

The invention relates to a TSP problem path planning method. The method comprises the following steps: initializing; reading the position and calculating the distance; initializing a population through a greedy algorithm; replacing the worst individuals with randomly generated individuals; calculating the fitness; selecting; crossing; performing variation; randomly performing simulated annealing on the plurality of individuals; calculating the fitness; giving the contemporary optimal solution and the variant solution thereof to the first individual and the second individual respectively; and iterating until the termination condition is met. The population generated by the greedy algorithm has randomness and high quality, and optimization can be accelerated. A plurality of worst individualsare replaced by randomly generated individuals, so that the influence of differential solutions is reduced, and precocity is avoided. Some better solutions can be found through simulated annealing, and precocity and local optimization are avoided. The storage of the optimal solution and the variant solution of the optimal solution retains excellent information and increases population diversity.According to the invention, a shortest access path can be effectively and quickly planned, so that the method is an effective method capable of providing path planning for the TSP problem.

Description

technical field [0001] The invention relates to a path planning method for a TSP problem, belonging to the technical fields of combination optimization and path planning. Background technique [0002] The traveling salesman problem, or TSP problem (Traveling Salesman Problem), is a well-known NP-hard problem in the field of combinatorial optimization, that is, its time complexity in the worst case increases exponentially with the increase of the problem size, so far An efficient algorithm in polynomial time has not yet been found. The TSP problem is generally described as: a traveling salesman wants to visit a given n cities, starting from the starting point, each city must be visited and can only be visited once, and finally returning to the starting point, it is necessary to plan a shortest visiting path that includes all n cities. The TSP problem is not just a traveling salesman problem, many other problems in real life, such as printed circuit drilling, optimization of ...

AI Technical Summary

Problems solved by technology

However, the traditional genetic algorithm has disadvantages such as slow convergence speed, easy premature convergence, and easy to fall into local optimum. When solving the TSP problem, the path obtained is relatively poor, or there are unreasonable situations such as multi-segment path intersection.

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