DocumentCode
3106528
Title
Using Genetic Algorithm Based on Composite Statistical Functions to Solve TSP by Nodes Regrouping
Author
Jie, Chen ; Ma Ya-hui
Author_Institution
Comput. Sch., Hubei Univ. of Technol., Wuhan, China
fYear
2011
fDate
16-18 Aug. 2011
Firstpage
1
Lastpage
3
Abstract
As the traditional evolutionary algorithms for large-scale TSP(Traveling Salesman Problem) produce so huge amount of paths vectors with random that the slow and premature convergence is nearly inevitable,this paper presents a novel evolutional algorithm based on hybrid probability distribution(EABHPD) with area partition strategy. The fundamental idea is to use evolutionary algorithm twice.Firstly the large-scale TSP is divided into several small-scale TSP, then each sub-TSP can be solved with EABHPD.With EABHPD, the rules of mutation are the combination of Gaussian probability distribution,Cauchy probability distribution and t probability distribution. This designed algorithm can get a good compromise of the desired pricision and computation cost, it also can avoid the premature convergence problem of the common evolutionary algorithms.Besides,the efficiency of our approach is manifested by the preliminary simulation experiment .
Keywords
Gaussian distribution; genetic algorithms; travelling salesman problems; Cauchy probability distribution; Gaussian probability distribution; area partition strategy; composite statistical functions; evolutionary algorithm based on hybrid probability distribution; genetic algorithm; nodes regrouping; paths vectors; t probability distribution; traveling salesman problem; Cities and towns; Convergence; Genetic algorithms; Greedy algorithms; Optimization; Process control; Search problems;
fLanguage
English
Publisher
ieee
Conference_Titel
Internet Technology and Applications (iTAP), 2011 International Conference on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-7253-6
Type
conf
DOI
10.1109/ITAP.2011.6006319
Filename
6006319
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