Planar array optimization by means of SNO and StudGA

In recent years there has been an increasing attention to novel evolutionary optimization techniques. A recently developed algorithm called Social Network Optimization (SNO), based on the emulation of decision making process in social network environments, is here considered and compared to Stud Genetic Algorithm (SGA). The design of a planar array is here addressed in order to compare their performances on a benchmark EM optimization problem. Reported results show their effectiveness in dealing with antenna optimization.