The array synthesis problem is modeled as a nonlinear optimization problem with the constraints of a reduced side lobe level (SLL). The goal is to impose deeper nulls in the interference direction of an uniform linear antenna arrays. This paper focuses on the array synthesis of adaptive nulling and side lobe level control which is a highly nonlinear problem fit for an evolutionary optimization algorithm. The limitations with the available optimization algorithms are mainly subjected to premature convergence and stagnation problem hence resulting in a non satisfactory array synthesis results. Hence, a new optimization technique known as Collective Social Behavior (CSB) is developed with concepts adopted from evolution of social behavior to address the current limitations to achieve a better result in array synthesis problem. The optimal set of weight coefficients for the linear antenna arrays is determined by the CSB algorithm by evaluating the objective function for the array synthesis problem. To establish the CSB algorithm as an efficient optimization tool for adaptive nulling applications, several numerical benchmark tests have been conducted. The simulation results reveal that the proposed CSB algorithm enhances the performance of array pattern synthesis with precise deeper nulls and suppressed side lobe levels.