Improved Flamingo Search Algorithm And Its Application In Antenna Optimal Design

With the rapid development of wireless communication technology,it is extremely important to optimize the performance of the antenna to meet the design goals.Traditional antenna optimization relies heavily on the professional knowledge and design experience of scientific researchers,and requires parameter scanning in electromagnetic simulation software,which is computationally intensive and time-consuming.Optimizing the design of antenna through intelligent algorithms combined with HFSS simulation becomes a more efficient antenna optimization method,and the stronger the search ability of the intelligent algorithm,the better the performance of the antenna can be found in the end.The main work of this paper is（1）In this paper,the flamingo search algorithm is optimized and improved.The chaotic initialization of the population is carried out through the Logistic mapping,and the nonlinear decreasing inertia weight and the Levy flight mechanism are integrated into the algorithm,which further strengthens the search ability of the improved algorithm and solves the problem of falling into a local optimal solution.The paper tests the improved algorithm through some classic test functions,and compares the results with the initial flamingo algorithm and other intelligent optimization algorithms to verify the superiority of the improved algorithm.（2）The paper builds the design framework about Co-simulation of Flamingo Search Algorithm for antenna optimization,which is divided into two modules:intelligent algorithm and electromagnetic calculation.The part of Matlab mainly includes intelligent algorithms,fitness functions and antenna design codes.The code about the design of antenna generates a vbs script file.The HFSS software performs antenna modeling and simulation according to the vbs script,and exports the performance results as a csv file for the fitness function to read,thereby realizing interactive co-simulation design.The paper takes the dual U-shaped antenna with two working frequency bands as an optimization case,and compares the optimization results with the results of the cuckoo algorithm optimization.The fitness value of the flamingo algorithm optimization is-771.15d B,which is better than the cuckoo optimization result of-758.44d B,indicating that the emerging flamingo algorithm can find better performance results in assisting in the design of antenna optimization due to its strong search ability.（3）The paper designs dual C butterfly antenna,which can cover 2.45GHz and 5.8GHz respectively,and have better dual working frequency bands.The antenna is optimized from two aspects through the flamingo algorithm and the improved flamingo algorithm.The value of S₁₁of the optimized antenna in scheme one is better in the high and low working frequency bands,and the improved flamingo algorithm has better results.In Solution two,the antenna optimized by the improved flamingo algorithm has an ultra-wide frequency band,from 2.08-7.93GHz（5.85GHz）,successfully combining the previous high and low working frequency bands.