Title :
An improved genetic algorithm for solving the Traveling Salesman Problem
Author_Institution :
Inf. Eng., Guangdong Ji Dian Polytech., Guangzhou, China
Abstract :
In this paper, on the basis of the original genetic algorithm, an improved genetic algorithm for the Traveling Salesman Problem (TSP) is proposed. Firstly, the diversity of species is ensured by amending the calculation method of the individual fitness. Secondly, the mutation operator is improved by the combination of shift mutation and insertion mutation. Before the crossover, the operator checks whether the degradation phenomenon will occur. Finally, experimental results further determine that above improvements provide a significant effect for solving the TSP.
Keywords :
computational complexity; genetic algorithms; travelling salesman problems; Darwinian genetic selection; TSP; biological evolution; combinatorial problem; computational model; improved genetic algorithm; individual fitness calculation method; insertion mutation; mutation operator improvement; shift mutation; specie diversity; traveling salesman problem; Cities and towns; Convergence; Genetic algorithms; Organisms; Sociology; Statistics; Traveling salesman problems; Genetic Algorithm; Optimization; Traveling Salesman Problem Solving;
Conference_Titel :
Natural Computation (ICNC), 2013 Ninth International Conference on
Conference_Location :
Shenyang
DOI :
10.1109/ICNC.2013.6818008