DocumentCode :
2371117
Title :
Traffic network distribution based on distribution center problem and genetic algorithm
Author :
Xu, Wei ; Shen, Ren-Jie ; Wu, Gui-Fang ; Zhou, Kang
Author_Institution :
Sch. of Math & Comput., Wuhan Polytech. Univ., Wuhan, China
fYear :
2012
fDate :
23-25 March 2012
Firstpage :
219
Lastpage :
223
Abstract :
The first traffic network distribution based on distribution center problem (TNDBDCP) is put forward, which can not be solved by traditional algorithms. In order to solve TNDBDCP, improved genetic algorithm is put forward based on the idea of global and feasible searching. In the improved genetic algorithm, chromosome is generated to use binary-encoding, and more reasonable fitness function of improved genetic algorithm is designed according to the characteristics of spanning tree and its cotree; in order to ensure the feasibility of chromosome, more succinct check function is introduced to three kinds of genetic operations of improved genetic algorithm (generation of initial population, parental crossover operation and mutation operation); three kinds of methods are used to expand searching scope of algorithm and to ensure optimality of solution, which are as follows: the strategy of preserving superior individuals is adopted, mutation operation is improved in order to enhance the randomness of the operation, crossover rate and mutation rate are further optimized. The validity and correctness of improved genetic algorithm solving MSTLCP are explained by a simulate experiment where improved genetic algorithm is implemented using C programming language. And experimental results are analyzed: selection of population size and iteration times determines the efficiency and precision of the simulate experiment.
Keywords :
C language; binary codes; cellular biophysics; genetic algorithms; genetics; iterative methods; trees (mathematics); C programming language; MSTLCP; TNDBDCP; binary-encoding; chromosome feasibility; chromosome generation; cotree; distribution center problem; feasible searching; fitness function; genetic algorithm; genetic operations; global searching; iteration times; mutation operation; mutation rate; population size; searching scope; spanning tree; succinct check function; superior individuals preservation; traffic network distribution; Biological cells; Educational institutions; Encoding; Genetic algorithms; Layout; Logistics; Time factors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Science and Technology (ICIST), 2012 International Conference on
Conference_Location :
Hubei
Print_ISBN :
978-1-4577-0343-0
Type :
conf
DOI :
10.1109/ICIST.2012.6221641
Filename :
6221641
Link To Document :
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