Intelligent transportation scheduling management method based on neural network genetic algorithm

Abstract

The invention discloses an intelligent transportation scheduling management method based on a neural network genetic algorithm. The method comprises the following steps: A1, establishing a data moduleaccording to a transportation scheduling management system; a2, generating an initial population G, wherein the initial population comprises N chromosomes, and the chromosome encodes the material M (Ma and/or Mb) according to the characteristics selected as required according to any length; constructing a neural network, wherein the delivery point is a neural network input node, the receiving point is an output node, and the transfer station is a hidden layer; a4, starting iteration. A neural network algorithm is added into the genetic algorithm, so that the defects of low calculation efficiency, easiness in falling into local optimum, difficulty in convergence and the like in the genetic algorithm are avoided, and premature convergence or a large number of iterative complex calculationsare avoided. The fitness evaluation function evaluates from two opposite angles of economic applicability e and time length t, and the two opposite functions are balanced with each other, so that a rapid and economic logistics scheduling scheme is realized.

Description

technical field [0001] The invention relates to the technical field of transportation pipelines, in particular to a high-pressure hydrogenation large-diameter super-thick seamless tee pipe fitting. Background technique [0002] The genetic algorithm is based on the principle of survival of the fittest in self-science, and was later cited in the optimization algorithm. The genetic operations performed in the evolution process include coding, selection, crossover, mutation, and survival of the fittest. There is no need for function derivatives and requirements The function is continuous. It simulates the phenomena of reproduction, crossover and gene mutation in the process of natural selection and natural inheritance. In each iteration, a group of candidate solutions is reserved, and a better individual is selected from the solution group according to a certain index. Genetic operators (selection, crossover and mutation) combine these individuals to generate a new generation o...

AI Technical Summary

Problems solved by technology

The fitness function is similar to the role of the environment in the evolution of organisms. Individuals with high fitness will produce more offspring in the reproduction process from generation to generation, while individuals with low fitness will gradually die out; but the computational efficiency of genetic algorithm Low, easy to fall into local optimum, difficult convergence and other deficiencies, whic...

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