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Vol. 165, Issue 2, February 2014, pp. 74-80

 

Bullet

 

Design of Hybrid Optimization Algorithm Targeting at Vehicle Routing for Large-Scale Outlets
 

1, 2 Li Shen, 1 Li Yuan Xiang, 3 Li Bo, 2 Xu Ning, 3 Xu Shengzhou

1 Computer Science College, Wuhan University, Wuhan, 430074, China
2 Computer Science College, Wuhan University of Technology, Wuhan, 430074, China
3 Computer Science College, Central-South University for Nationalities, Wuhan, 430074, China

E-mail: libo_hust@126.com

 

Received: 20 November 2013 /Accepted: 28 January 2014 /Published: 28 February 2014

Digital Sensors and Sensor Sysstems

 

Abstract: Vehicle route planning is a NP-hard issue in logistics. This paper has designed a hybrid optimization algorithm based on ant colony algorithm, genetic algorithm and chaos algorithm to satisfy the large scale network requirements in practical applications. The innate advantages of the optimal route of ant colony algorithm has been fully used to establish good gene pool so as to take advantage of the genetic crossover and mutation of genetic algorithm and the randomness and ergodicity of chaos algorithm. Further optimization has been made to the individuals and populations of the ant colony algorithm and adaptive pheromone update mechanism has been established to effectively solve some practical problems concerning large-scale data file structure, such as the optimization, multiple time windows, line profile, and traffic impact and so on. A comparison of the efficiency of the algorithm shows that the algorithm proposed in the paper is of advantage in terms of time complexity and stability, which can effectively cope with large-scale data with over 1000 outlets, cater for other practical requirements and put into practical application.

 

Keywords: Hybrid optimization, Vehicle route planning, Ant colony algorithm, Genetic algorithm, Chaos algorithm.

 

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