Optimization model method based on generative adversarial network and application

Abstract

The invention discloses an optimization model method based on a generative adversarial network and an application, called GAN-O, the method comprises the following steps: expressing the application (such as logistics distribution optimization) as a function optimization problem; establishing a function optimization model based on the generative adversarial network according to the test function and the test dimension of the function optimization problem, including constructing a generator and a discriminator based on the generative adversarial network; training a function optimization model; carrying out iterative computation by utilizing the trained function optimization model to obtain an optimal solution; therefore, the optimization solution based on the generative adversarial network is realized. According to the method, a better local optimal solution can be obtained in a shorter time, so that the training of the deep neural network is stable, and the method has more excellent local search capability. The method can be used for many application scenarios such as logistics distribution problems which can be converted into function optimization problems in reality, the application field is wide, a large number of actual problems can be solved, and the popularization and application value is high.

Description

technical field [0001] The invention relates to the technical field of computational model optimization, in particular to a novel optimization model method and application based on a generative confrontation network. Background technique [0002] Function optimization problems have always been one of the most important problems in the fields of mathematics and computer science. In reality, many application scenarios can be transformed into function optimization problems, such as logistics distribution problems, deep network optimization problems, etc. The application field of function optimization problem is very broad, and it can solve a large number of practical problems. [0003] For function optimization problems, the existing algorithms are mainly gradient-based algorithms. The disadvantage of this type of algorithm is that it is very easy to fall into local extremum. For some problems, such as neural network optimization, the local extremum usually has a good enough e...

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Problems solved by technology

[0007] In order to overcome the shortcomings of the above-mentioned prior art, the present invention provides a new optimization model method and application based on generative confrontation netwo

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