Improved TLBO Algorithm And Its Application In Antenna Optimization Design

With the development of society and the advancement of science and technology,many scientific,economic and engineering problems encountered in practice are characterized by multi-polarization,nonlinearity,strong constraints,high dimension and difficult modeling.Traditional optimization algorithms have been difficult to obtain the optimal solution,therefore,the search for an effective and efficient optimization method for solving more complex optimization problems is very important,and has become a research hotspot of many scholars.As an important branch of the meta heuristic algorithm,the group intelligent optimization algorithm is favored by many scholars because of its strong adaptability and its advantages in solving complex optimization problems.As a kind of emerging group intelligent optimization algorithm proposed in 2011,the teaching-learning-based optimization algorithm has the advantages of simple algorithm structure,easy understanding,less algorithm parameters and strong convergence ability.However,there is also a slow convergence speed of the algorithm.It is easy to fall into local optimum,and has shortcomings such as poor optimization results for high-dimensional problems.Therefore,this thesis improves the standard teaching-learning-based optimization algorithm,and applies the improved teaching-learning-based optimization algorithm to the array antenna pattern synthesis.The field of optimized design of microstrip slot antennas.The main research results of the thesis are as follows:(1)The basic principles and mathematical models of the algorithm are introduced and the standard teaching-learning-based optimization algorithm are introduced.The comparison between the two common cluster intelligent optimization algorithms and the particle swarm optimization algorithm and the invasive weed optimization algorithm is used to summarize the teaching-learning-based optimization algorithm.The advantages and disadvantages of the algorithm provide ideas for subsequent algorithm improvements.(2)In order to improve the development and search ability of the algorithm and the ability to search for high-dimensional complex problems,this thesis improves the algorithm and proposes a teaching-learning-based optimization algorithm for small class parallel teaching.The algorithm changes the method of algorithm initialization and divides the students into two classes.One class joins the student preparation stage before the teaching stage to improve the development ability of the algorithm.Another class introduced the Metropolis guidelines and added a self-study phase before the teaching phase to improve the search capabilities of the algorithm.Students who have better grades in the two classes are selected to form a temporary class,and the outstanding students are selected to replace the students with poor grades in the two small classes.Each iteration,the students in the temporary class are updated once according to the grades until the jump loop condition is met.The effectiveness of the algorithm is verified by simulation experiments on the test function.(3)Applying the improved teaching and learning optimization algorithm to the problem of array antenna pattern synthesis,after experimental analysis,it can be seen that compared with the traditional analytical method and the standard teaching and learning optimization algorithm,the improved teaching and learning optimization The algorithm can get better results.(4)The improved teaching-learning-based optimization algorithm is applied to the optimization design field of the microstrip slot antenna,which realizes the automation of the antenna optimization design and greatly improves the design efficiency of the antenna.