Development a new mutation operator to solve the Traveling Salesman Problem by aid of Genetic Algorithms
Küçük Resim Yok
Tarih
2011
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
PERGAMON-ELSEVIER SCIENCE LTD
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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%. (C) 2010 Elsevier Ltd. All rights reserved.
Açıklama
Anahtar Kelimeler
Genetic Algorithm, Mutation operator, Greedy methods, Traveling Salesman Problem, Optimization
Kaynak
EXPERT SYSTEMS WITH APPLICATIONS
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
38
Sayı
3
