Application Of Improved Gray Wolf Algorithm In Antenna Design

With the development of society and the progress of science and technology,many scientific,economic and engineering problems that people encounter in their lives have the characteristics of multi polarization,non-linear,strong constraints,high dimensions,etc.the traditional optimization algorithm has been difficult to find the optimal solution of the problem.Therefore,it is very important to find an effective and efficient optimization method to solve more complex optimization problems,which has become a research hotspot of many scholars.The cluster intelligent optimization algorithm has been favored by many scholars because of its strong self-adaptability and its advantages in solving complex problems.As a classical cluster intelligent optimization algorithm,grey wolf optimization algorithm has the advantages of small amount of calculation and easy to realize,but it still has the disadvantages of premature convergence,low convergence accuracy and low convergence speed in the face of complex problems.Therefore,this paper improves the standard gray wolf optimization algorithm,and applies the improved gray wolf optimization algorithm to the microstrip leaky wave antenna and the compact dual frequency MIMO antenna which can be used in WLAN.The main research contents and results of the paper are as follows:(1)This paper introduces the standard gray wolf optimization algorithm from two aspects,one is the basic principle of the algorithm,the other is the mathematical model.It compares the performance of the standard gray wolf optimization algorithm with that of the standard whale optimization algorithm and the standard Dragonfly optimization algorithm,summarizes the shortcomings of the gray wolf algorithm,and paves the way for the later improvement of the algorithm.(2)In order to improve the global optimization ability and convergence speed of the standard gray wolf optimization algorithm,this paper improves the algorithm as follows: First,the population is initialized using the uniform distribution of Hammersley sequences.Secondly,the vertical and horizontal crossover algorithm is used to perform horizontal crossover operations on different individuals,vertical crossover operations on different dimensions of the same individual,and finally compete with the parent to select the better individual to improve the convergence speed.Finally,the convergence factor ‘a' is adjusted by a nonlinear convergence strategy to balance the local and global exploration capability of the algorithm.(3)Using the standard test function,this paper compares the improved gray wolf optimization algorithm with the standard gray wolf optimization algorithm and other improved gray wolf optimization algorithm to verify the effectiveness of the improved strategy.(4)The improved algorithm is applied to the microstrip leaky wave antenna and the compact dual frequency MIMO antenna which can be used in WLAN.The Kriging agent model is used to simulate the performance of the antenna.Through analysis and comparison,it is found that the improved algorithm improves the performance of the antenna.