Sparse array design by means of Social Network Optimization

Abstract

This paper presents a recently developed algorithm based on the emulation of decision making process in social network environments, called Social Network Optimization (SNO). The design of a sparse array is here addressed in order to assess SNO's performance on a benchmark EM optimization problem. Reported results show its effectiveness in dealing with EM problems

Shaped-Beam Reflectarray Design by Means of Social Network Optimization (SNO)

“…The SNO performance has already been assessed through its comparison with other EAs (such as GA and PSO) when applied to standard benchmarks and to different antenna problems [30,36]: the results summarized in these papers confirm that the SNO outperformed, in most of the analyzed cases, the other considered methods in terms of convergence rate, solution quality and reliability. These features make the SNO suitable for the solution of the problem considered here, characterized by a high number of variables and a computationally expensive cost function.…”

“…In this paper, the design of a shaped-beam RA with a cosecant squared radiation pattern is based on the use of an efficient EA, the Social Network Optimization (SNO) [28], which mimics the behavior of the people interacting through a social network [29]. This algorithm has been previously applied to different antenna optimization problems, ranging from the sparse array optimization [30] to the design of a pencil-beam reflectarray [31], and of a transmitarray [32]: in all the cases it showed good convergence and reliability. Moreover, in [33], some very preliminary numerical results on its application to the design of a center-fed shaped-beam RA are summarized: in view of them, here a more complex, offset-fed configuration is considered, and the design procedure is also validated through the experimental characterization of a prototype.…”