Spiral antenna design method based on adaptive evolution optimization algorithm

Abstract

The invention provides a spiral antenna design method based on an adaptive evolution optimization algorithm. The spiral antenna design method comprises the following steps: firstly, establishing a structural model of a spiral antenna and an optimization mathematical model corresponding to the structural model; then, optimizing the antenna structure parameters in the optimization mathematical modelby adopting a reinforcement learning-based adaptive evolution optimization algorithm to obtain final optimized antenna structure parameters; and finally, according to the antenna structure parameters, adjusting the structure model to obtain the designed spiral antenna. The beneficial effects of the method are as follows: the parameters and the operation operator of the evolution algorithm are controlled by utilizing reinforcement learning; therefore, the self-adaptive differential evolution algorithm is realized, and a large amount of intermediate result data generated in the operation process of the self-adaptive differential evolution algorithm is used for guiding subsequent parameter setting of the evolution algorithm, so that the optimal antenna structure parameter of the spiral antenna is obtained, the efficiency is high, and the optimization performance is good.

Description

technical field [0001] The invention relates to the field of antenna design, in particular to a method for designing a helical antenna based on an adaptive evolution optimization algorithm. Background technique [0002] Evolutionary computing is an intelligent optimization technology based on the ideas of survival of the fittest and survival of the fittest in Darwin's evolution theory. Evolutionary computing takes the solution of the optimization problem as the gene of the individual, and the target value of the optimization problem as the performance evaluation of the individual. During the algorithm, it uses crossover and mutation operations similar to those in biological genetics to generate new solutions, and then uses the idea of ​​survival of the fittest , individuals with better performance are more likely to be retained in the next generation of calculations, so that the optimal solution to the problem can be obtained. The basic flow chart of the evolutionary algori...

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Problems solved by technology

[0006] The antenna design problem can be regarded as a kind of optimization problem that takes the antenna structure as the search space and seeks the optimal performance of the antenna. Classical optimization methods are difficult to solve this kind of optimization problem. The existing classic antenna exa...

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