Open AccessResearch on Material Allocation Path Based on Hopfield Neural Network and Simulated Annealing Hybrid Algorithm
Author(s) -
Junpeng Xi,
Xudong Zhao,
Yaowu Zhu,
Xiao Baoqiang,
Shun Chen
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1682/1/012003
Subject(s) - simulated annealing , artificial neural network , algorithm , computer science , hybrid algorithm (constraint satisfaction) , hopfield network , adaptive simulated annealing , convergence (economics) , path (computing) , mathematical optimization , artificial intelligence , mathematics , constraint satisfaction , probabilistic logic , economic growth , economics , programming language , constraint logic programming
When a natural disaster occurs, in order to ensure the basic material needs of the affected people, the delivery of emergency materials after the disaster occupies a very important position. Aiming at the defects that the Hopfield neural network algorithm is easy to fall into local optimum and the simulated annealing algorithm convergence speed is too slow, a hybrid neural network algorithm (SA-HNN) is proposed. Combining the advantages of Hopfield neural network and simulated annealing algorithm, the simulated annealing algorithm is the mechanism of receiving a poor solution with a certain probability is applied to the Hopfield neural network algorithm, which overcomes the defect that the neural network is easy to fall into the local optimal. Based on this hybrid algorithm, the vehicle distribution path optimization problem is solved. Compared with the traditional neural network algorithm, the algorithm is improved Calculation efficiency and accuracy.