Multiconstrained network intensive vehicle routing adaptive ant colony algorithm in the context of neural network analysis
type: Item
instance of: scholarly article
title: Multiconstrained network intensive vehicle routing adaptive ant colony algorithm in the context of neural network analysis
zbMATH Open document ID: 1373.93045
DOI: 10.1155/2017/8594792
author: Shaopei Chen
author: Ji Yang
author: Yong Li
author: Jingfeng Yang
published in: Complexity
publication date: 2017-10-26T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 93A15
Mathematics Subject Classification ID: 92B20
Mathematics Subject Classification ID: 90C59
Mathematics Subject Classification ID: 90B20
zbMATH DE Number: 6798464
zbMATH Keywords: vehicle routing adaptive ant colony algorithm
zbMATH Keywords: neural network
zbMATH Keywords: local optimal solution
zbMATH Keywords: global optimal solution
zbMATH Keywords: pheromone adaptive strategy
zbMATH Keywords: large-scale multiconstrained vehicle routing
zbMATH Keywords: complex traffic network systems
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/8594792
OpenAlex ID: W2756205897
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description: scientific article; zbMATH DE number 6798464