Development a new mutation operator to solve the Traveling Salesman Problem by aid of Genetic Algorithms |
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| Authors: | Murat Albayrak Novruz Allahverdi |
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| Affiliation: | 1. Department of Information Technology, Széchenyi István University, Gy?r, Hungary;2. Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary;3. Department of Logistics, Széchenyi István University, Gy?r, Hungary;1. School of Mathematics, Statistics and Computer Science, University of Kwazulu-Natal, Westville Campus, Private Bag X54001, Durban 4000, South Africa;2. Faculties of Mathematics and Computer Science, West University of Timisoara, Timisoara, Romania |
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| Abstract: | In this study, a new mutation operator has been developed to increase Genetic Algorithm (GA) performance to find the shortest distance in the known Traveling Salesman Problem (TSP). We called this method as Greedy Sub Tour Mutation (GSTM). There exist two different greedy search methods and a component that provides a distortion in this new operator. The developed GSTM operator was tested with simple GA mutation operators in 14 different TSP examples selected from TSPLIB. The application of this GSTM operator gives much more effective results regarding to the best and average error values. The GSTM operator used with simple GAs decreases the best error values according to the other mutation operators with the ratio of between 74.24% and 88.32% and average error values between 59.42% and 79.51%. |
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