Paper Info
Title
Multiconstrained Network Intensive Vehicle Routing Adaptive Ant Colony Algorithm in the Context of Neural Network Analysis.
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.
Year
DOI
Venue
2017
10.1155/2017/8594792
COMPLEXITY
Field
DocType
Volume
Convergence (routing),Ant colony optimization algorithms,Neural network analysis,Vehicle routing problem,Adaptive strategies,Scheduling (computing),Artificial intelligence,Artificial neural network,Machine learning,Mathematics
Journal
2017
ISSN
Citations 
PageRank 
1076-2787
1
0.36
References 
Authors
8
4
Name
Order
Citations
PageRank
Shaopei Chen110.36
Ji Yang295.93
Yong Li331.41
Jingfeng Yang4618.34