Improved genetic algorithm-based traveling salesman problem solving method

A technique for improving genetic algorithm and traveling salesman problem, which is applied in the field of genetic algorithm to achieve high efficiency

Inactive Publication Date: 2017-09-01
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

[0005] In order to overcome the shortcomings and deficiencies of the prior art, the present invention provides a method for solving the traveling salesman problem based on the improved genetic algorithm. On the basis of the traditional genetic algorithm, the traveling salesman problem is optimized to achieve the premature convergence of the improved algorithm. Disadvantages and the purpose of optimizing search efficiency

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  • Improved genetic algorithm-based traveling salesman problem solving method
  • Improved genetic algorithm-based traveling salesman problem solving method
  • Improved genetic algorithm-based traveling salesman problem solving method

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[0037] Such as figure 1 As shown, the present invention provides a method for solving the traveling salesman problem based on the improved genetic algorithm, and its main steps are as follows.

[0038] 1) Coding; 2) Setting of initial population; 3) Setting of fitness function and calculation of population fitness; 4) Genetic operation (selection, crossover, mutation); Parameters to stop at time, including population size, crossover probability, mutation probability, maximum number of iterations, etc.). The text description of the algorithm flow is as follows:

[0039] Step 1: For a given problem, determine the coding form of the problem and code the problem; the present invention is aimed at the TSP problem, and the path is coded using a decimal number string, and on its search space U, the fitness function f(x) is defined, And define the population size N, crossover probability P c , the mutation probability P m , and the number of iterations T;

[0040] Step 2: In U, r...

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Abstract

The invention discloses a method for solving the traveling salesman problem based on an improved genetic algorithm. The steps include: aiming at the TSP problem, encoding the path using a decimal number string; calculating the total length, and then judging the total length; after encoding On the search space U of the decimal number string path, define the fitness function f(x), and define the population size n, the crossover probability Pc, the mutation probability Pm and the number of iterations T; in the search space U, randomly generate n individuals s1, s2, s3, ..., sn, constitute the initial population S0 = {s1, s2, s3, ..., sn}, set the current iteration number t = 0; according to the fitness function f(x), evaluate the individual fitness in the population , if t<T, then end the step, otherwise perform the genetic operation step; the individual with the highest fitness obtained through the genetic operation step is the optimal solution of the traveling salesman problem solving method. Based on the traditional genetic algorithm, the present invention optimizes the traveling salesman problem to achieve the purpose of improving the shortcoming that the algorithm is prone to premature convergence and optimizing the search efficiency.

Description

technical field [0001] The invention relates to the application of genetic algorithms in the technical field of computers, in particular to a method for solving traveling salesman problems based on improved genetic algorithms. Background technique [0002] In the field of computer applications, Genetic Algorithm (GA) was first proposed in 1975 by Professor J. Holland of the University of Michigan in the United States. It is a computational model that simulates the process of biological evolution by drawing on Darwin's theory of evolution and the theory of natural selection. According to the genetic mechanism, the solution of the problem is processed similar to the natural evolution process (survival of the fittest), so that the solution of the problem evolves in the direction of adapting to the environment (problem), and then obtains the optimal solution. Simple, universal, robust and suitable for parallel processing are the characteristics of genetic algorithm. Genetic Al...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06N3/12
CPCG06Q10/047G06N3/126
Inventor 胡劲松李湘宁
Owner SOUTH CHINA UNIV OF TECH
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