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2013•Transactions of Beijing Institute of TechnologyRequires access

An Improved Genetic Algorithm for Solving Travel Salesman Problem

Jing Chen

Open publisher page 5 citations

Abstract

Standard genetic algorithm in solving the traveling salesman problem(TSP) is not efficient since it is easy to fall into local optimal solution.To improve the efficiency of genetic algorithm,this paper presents an improved genetic algorithm.First,according to the diversity of individuals and the population distribution,the method to determine the cut-off algebraic of genetic algorithm is proposed.Second,by adding initialization information and improving cross-operator,the accuracy and convergence of the genetic algorithm could be improved.

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What this paper is about

Standard genetic algorithm in solving the traveling salesman problem(TSP) is not efficient since it is easy to fall into local optimal solution.To improve the efficiency of genetic algorithm,this paper presents an improved genetic algorithm.First,according to the diversity of individuals and the population distribution,the method to determine the cut-off algebraic of genetic algorithm is proposed.Second,by adding initialization information and improving cross-operator,the accuracy and convergence of the genetic algorithm could be improved.

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OpenAlex reports 5 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Standard genetic algorithm in solving the traveling salesman problem(TSP) is not efficient since it is easy to fall into local optimal solution.To improve the efficiency of genetic algorithm,this paper presents an improved genetic algorithm.First,according to the diversity of individuals and the population distribution,the method to determine the cut-off algebraic of genetic algorithm is proposed.Second,by adding initialization information and improving cross-operator,the accuracy and convergence of the genetic algorithm could be improved.

Key concepts: Travelling salesman problem, Genetic algorithm, Initialization, Population-based incremental learning, Mathematical optimization, Genetic operator, Cultural algorithm, Computer science

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