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

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  • Shaopei Chen
  • Ji Yang
  • Yong Li
  • Jingfeng Yang

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.

Suggested Citation

  • Shaopei Chen & Ji Yang & Yong Li & Jingfeng Yang, 2017. "Multiconstrained Network Intensive Vehicle Routing Adaptive Ant Colony Algorithm in the Context of Neural Network Analysis," Complexity, Hindawi, vol. 2017, pages 1-9, September.
    • Handle: RePEc:hin:complx:8594792
    DOI: 10.1155/2017/8594792
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    Cited by:

    1. Weiqin Ying & Bin Wu & Yu Wu & Yali Deng & Hainan Huang & Zhenyu Wang, 2019. "Efficient Conical Area Differential Evolution with Biased Decomposition and Dual Populations for Constrained Optimization," Complexity, Hindawi, vol. 2019, pages 1-18, February.

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