Optimization of Antenna Arrays for SLL Reduction Towards Pareto Objectivity Using GA Variants

One of the most striking aspects of nature inspired algorithms is their capability of reaching a pare to front, for a set number of objectives, with much lesser computational cost than the classical ones, this is primarily due to the intrinsic intelligence that they inherit from nature. In this paper, as a first example, optimization of linear antenna arrays with dipole element pattern is exemplified for side lobe (SLL) reduction with fixed main-lobe beam width using real coded genetic algorithm (RGA), though multi goal optimization seems to be possible with proper cost/fitness function formulation in linear arrays, such a task becomes extremely difficult when it comes to planar arrays, this calls for the use of multi objective variants of an algorithm to reach the pare to objectivity, as a second example non-dominated sorting genetic algorithm II (NSGA2) is considered as the optimization algorithm for SLL reduction and fixed main-lobe directivity for concentric regular hexagonal antenna arrays (CRHAA). The results show good outcome with respect to side lobe reduction and directivity.