Optimal sidelobes reduction and synthesis of circular array antennas using hybrid adaptive genetic algorithms

In this article, a hybrid optimization method has been proposed consisting of Adaptive Genetic Algorithms (AGAs) and Constrained Nonlinear Programming (NLP) to solve the problems of performance optimization of circular array antenna consist of parallel center feeding short dipoles elements with two complex nonlinear optimization problems. In the first problem, the hybrid optimization algorithm is used to reduce the value of sidelobe level in the circular array radiation pattern by finding the optimal values of the excitation coefficients of each element in the circular array. In the second problem, a synthesis of circular array with different forms of the desired radiation pattern is considered. Several examples are considered here to verify the validity of this method. The results obtained by this method show that it is possible to obtain an array radiation pattern with low sidelobe level of -40dB in the first problem. In the second problem, it is shown that it is possible to obtain a wide flat main lobe of 60^o beam width, and two nulls on both sides of the main lobe with 10^o width for each. Comparisons were made between the results of this method and the results obtained by Standard Genetic Algorithm (SGA), and it is clearly shown that this method is more efficient and flexible in solving the problems of performance optimization of circular array antenna.