Abstract
In this work, an innovative approach for the design of a shaped-beam reflectarray (RA) is presented. It is based on the use of a novel evolutionary algorithm (EA), named Social Network Optimization (SNO), that presents good capabilities in terms of convergence and reliability, and therefore it is suitable for optimizing a complex problem such as the one of interest. The full-wave analysis of a small–medium configuration designed with the proposed approach and the experimental characterization of a prototype proved the effectiveness of the adopted method.
Highlights
In recent years, the increasing complexity of many engineering problems, involving a huge number of degrees of freedom, and the enlarging of the available computational capabilities, increased the use of pseudo-stochastic optimization algorithms: they are able to manage a high number of independent parameters and to find an optimal solution in most cases, but at the cost of high computational effort, which is strictly related to the complexity of the mathematical model used to properly describe the problem to be solved.Among the pseudo-stochastic approaches, evolutionary algorithms (EAs) have been applied successfully to different families of problems: they are derivative-free, global optimization algorithms inspired by biological interaction and evolution [1]
The information sharing and selection process requires one to update his opinion, and the post population, at each iteration; this is performed through the action of two different groups of influencers that are active for the users: the friends, who evolve working on proximity rules, on the basis of the opinions; and the trusted, who are selected according to the visibility values of their posts
EAs when applied to standard benchmarks and to different antenna problems [30,36]: the results summarized in these papers confirm that the Social Network Optimization (SNO)
Summary
The increasing complexity of many engineering problems, involving a huge number of degrees of freedom, and the enlarging of the available computational capabilities, increased the use of pseudo-stochastic optimization algorithms: they are able to manage a high number of independent parameters and to find an optimal solution in most cases, but at the cost of high computational effort, which is strictly related to the complexity of the mathematical model used to properly describe the problem to be solved. 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.
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