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Order crossover multi-filial-generation genetic algorithm for solving traveling salesman problem

A technique of traveling salesman problem and genetic algorithm, applied in the field of applied artificial intelligence

Inactive Publication Date: 2015-03-25
NORTHEAST AGRICULTURAL UNIVERSITY
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Problems solved by technology

[0004] Aiming at the existing problems of using the genetic algorithm to solve the traveling salesman problem, the present invention improves the existing genetic algorithm and proposes a sequential crossover multi-offspring genetic algorithm for solving the traveling salesman problem
The number of offspring generated by the crossover of the sequential crossover multi-offspring genetic algorithm is significantly more than that of the existing genetic algorithm for solving the traveling salesman problem

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  • Order crossover multi-filial-generation genetic algorithm for solving traveling salesman problem
  • Order crossover multi-filial-generation genetic algorithm for solving traveling salesman problem
  • Order crossover multi-filial-generation genetic algorithm for solving traveling salesman problem

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Embodiment Construction

[0077] Embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0078] The sequential crossover multi-offspring genetic algorithm for solving the traveling salesman problem proposed by the present invention specifically includes the following steps:

[0079] (1) The biological theoretical basis and mathematical ecology theoretical basis of the sequential crossover multi-offspring genetic algorithm for solving the traveling salesman problem.

[0080] According to the theoretical basis of biology and mathematical ecology, the invention proposes a sequential crossover multi-offspring genetic algorithm for solving the traveling salesman problem.

[0081] (2) The evolutionary strategy of the sequential crossover multi-offspring genetic algorithm for solving the traveling salesman problem.

[0082] The evolutionary strategy of the sequential crossover multi-offspring genetic algorithm for solving the traveling salesman ...

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Abstract

The invention discloses an order crossover multi-filial-generation genetic algorithm for solving the traveling salesman problem. Firstly, according to a biological evolution principle and a mathematical ecology theory, the order crossover multi-filial-generation genetic algorithm for solving the traveling salesman problem is provided, and an order-crossover-based multi-filial-generation generation method is given out. The number of filial generation individuals generated by the order crossover multi-filial-generation genetic algorithm is obviously increased, so that population competition is more intense, the possibility of generating the excellent individuals is accordingly increased, and the performance of the genetic algorithm is better improved. Calculation results of two examples in a TSPLIB show that the operating rate of the order crossover multi-filial-generation genetic algorithm is obviously improved, and the number of iterations is obviously decreased, so that the effectiveness of the order crossover multi-filial-generation genetic algorithm for solving the traveling salesman problem is verified.

Description

Technical field [0001] The sequence of the problem of solving the problem of travel providers is a cross -child generation genetic algorithm, which is the field of artificial intelligence technology. Background technique [0002] Travelling Salesman Privem (TSP) is one of the well -known issues in the field of combination optimization and mathematics.The general mention of the TSP question is: a businessman will arrive n To sell goods in a city, n The distance between any two cities in a city is known. The traveler starts from a certain city, looking for a journey that can go through each city once and only once, and finally return to the original city.The shortest route.The TSP problem has been proved to be a typical NP problem.Because the TSP problem has the advantages of simple form and easy understanding, it is widely used in power grid planning, network optimization, pipeline laying, and logistics scheduling.Therefore, solving the TSP problem has high practical value.Since K...

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Application Information

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IPC IPC(8): G06N3/12G06Q10/04
Inventor 王吉权田占伟王福林何梦莹
Owner NORTHEAST AGRICULTURAL UNIVERSITY
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