Research On Shortest Path Optimal Algorithm Between Two Points

The shortest path selection is a basic optimization problem,which is widely used in automobile navigation,automatic path finding,artificial robot,Internet of things,router addressing and so on.The classical algorithms of shortest path selection,such as Dijkstra(Dijkstra)algorithm,A* algorithm and genetic algorithm,have their respective advantages.However,with the explosive growth of location information in the application field,the classical algorithm has been unable to meet the application requirements of efficiency and quality.This paper analyzes the pulse coupled neural network(PCNN)and basic principle,the mathematical model and made the following research work: 1,Analysis PCNN algorithm in a large-scale network model,low operation efficiency,increase the neurons trigger source optimization method is proposed,by increasing the automatic wave of parallel transmission of achieve the goal of shorter operation time;2,PCNN algorithm traversal search all nodes in the network,resource waste,low efficiency of faults,thus put forward the direction of the introduction of optimization methods,artificially set the weights of network,improve the automatic wave to end the spread of the priority,save search space resources,to achieve the purpose of improving searching efficiency;3,Combining with an instance of the search,pulse coupled neural network combined with the convolution of the network optimization scheme,by reducing the image information method to reduce the search space,achieve the goal of improve the efficiency of search.In order to verify the above optimization scheme,the results of MATLAB simulation experiment are concluded: 1.Compared with the average PCNN algorithm,the average PCNN algorithm is reduced by 33% compared with the average PCNN algorithm;2.The optimization scheme of the introduction direction can reduce the search time by 15% compared with the general PCNN algorithm in the large-scale network model;3.PCNN algorithm combined with convolution network optimization algorithm reduced the search time by 87.2% compared with PCNN algorithm.