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Complexity
Volume 2017, Article ID 8594792, 9 pages
https://doi.org/10.1155/2017/8594792
Research Article

Multiconstrained Network Intensive Vehicle Routing Adaptive Ant Colony Algorithm in the Context of Neural Network Analysis

1School of Public Administration, Guangdong University of Finance and Economics, Guangzhou, China
2Open Laboratory of Geo-Spatial Information Technology and Application of Guangdong Province, Guangzhou Institute of Geography, Guangzhou 510070, China
3Guangzhou Yuntu Information Technology Co., Ltd., Guangzhou 510532, China

Correspondence should be addressed to Yong Li; nc.ca.sadg@gnoyil and Jingfeng Yang; moc.621@gnaygnefgnij

Received 21 June 2017; Accepted 9 August 2017; Published 18 September 2017

Academic Editor: Yanan Li

Copyright © 2017 Shaopei Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Neural network models have recently made significant achievements in solving vehicle scheduling problems. Adaptive ant colony algorithm provides a new idea for neural networks to solve complex system problems of multiconstrained network intensive vehicle routing models. The pheromone in the path is changed by adjusting the volatile factors in the operation process adaptively. It effectively overcomes the tendency of the traditional ant colony algorithm to fall easily into the local optimal solution and slow convergence speed to search for the global optimal solution. The multiconstrained network intensive vehicle routing algorithm based on adaptive ant colony algorithm in this paper refers to the interaction between groups. Adaptive transfer and pheromone update strategies are introduced based on the traditional ant colony algorithm to optimize the selection, update, and coordination mechanisms of the algorithm further. Thus, the search task of the objective function for a feasible solution is completed by the search ants. Through the division and collaboration of different kinds of ants, pheromone adaptive strategy is combined with polymorphic ant colony algorithm. It can effectively overcome some disadvantages, such as premature stagnation, and has a theoretical significance to the study of large-scale multiconstrained vehicle routing problems in complex traffic network systems.