Comparative analysis of the efficiency of the genetic algorithm when modifying the mutation operator in the traveling salesman problem
Authors: Domanov K.I.  
Published in issue: #1(66)/2022  
DOI: 10.18698/2541800920221760  
Category: Informatics, Computer Engineering and Control  Chapter: System Analysis, Control, and Information Processing, Statistics 

Keywords: traveling salesman problem, genetic algorithm, graph, multipoint mutation, generation, extremum, vertices, algorithm efficiency, Python 

Published: 31.01.2022 
The author studied the influence of multipoint mutation on the result of the work of the genetic algorithm in solving the traveling salesman problem. In this work, the "greedy" strategy of the crossover operator and two types of mutation operators are applied, point and multipoint. A point mutation is a type of mutation in which a random vertex is selected and inserted at a random location in the sequence. The essence of multipoint mutation is to dynamically change the number of vertices subject to the mutation operation, depending on the number of vertices in the problem under consideration and the ordinal number of the current population. The software that implements this algorithm has been developed. The conducted studies have shown that algorithms with different types of mutations work approximately the same on problems of small dimension. However, with an increase in the number of vertices and the number of generations, the proposed multipoint mutation mechanism showed greater efficiency.
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