Sparse antenna array synthesis using multi-objective optimization

The process of sparse antenna array synthesis involves the simultaneous minimization of the number of mutually conflicting parameters, such as peak sidelobe level and first null beam width. This necessitates the development of a multi objective optimization process which will provide the best compromised solution based on the application at hand. In this paper multi-objective optimization is achieved using the non-dominating sorting genetic algorithm of NSGA-II. This approach yields much more improved results as compared to single objective optimization approach and at the same time it offers flexibility in choosing the solution based on the Pareto front.