Abstract

Reflectarray antennas are low-profile high-gain systems widely applied in the aerospace industry. The increase in their application is leading to the problem of getting more advanced performance while keeping the system as simple as possible. In these cases, their design cannot be conducted via analytical methods, thus evolutionary optimization algorithms are often implemented. Indeed, the design is characterized by the presence of many local minima, by high number of design variables, and by the high computational burden required to evaluate the antenna performance. The purpose of this paper is to develop, implement, and test a complete Optimization Environment that can be applied to achieve high scanning capabilities with a reflectarray. The design of the optimization environment has been selected to be flexible enough to be applied also with other different algorithms.

Highlights

  • In recent years, Evolutionary Algorithms (EAs) have been successfully applied to many engineering problems thanks to their capability to find optimal solutions in nonlinear and multimodal problems [1]

  • This algorithm has been widely studied and applied: its performance is highly dependent on the specific selection of the parameters and, with respect to Genetic Algorithms (GAs), it is characterized by an higher convergence rate that in some cases leads to a premature stagnation in local minima

  • In this paper a complete Optimization Environment has been designed to improve the results of the design optimization for a reflectarray antenna capable of beam scanning

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Summary

Introduction

Evolutionary Algorithms (EAs) have been successfully applied to many engineering problems thanks to their capability to find optimal solutions in nonlinear and multimodal problems [1]. In the case of more complex requirements in terms of radiation pattern, optimization methods are fundamental In this context, Evolutionary Algorithms (EAs) are very valuable tools. A recent problem in reflectarray antenna design is to improve their scanning capabilities, i.e., the possibility to modify the direction of the radiation pattern main beam [21]. In our paper this capability is achieved by steering the feeder with respect to a fixed reflector.

Antenna Geometry
Performance Parameters
Optimization Environment
Differential Evolution
Genetic Algorithm
Biogeography Based Optimization
Particle Swarm Optimization
Social Network Optimization
Antenna Optimization Results and Discussion
Feasibility Function
Objective function calls
Cost Function Parameter Definition
Algorithm Comparison
Analysis of the Final Solution
Conclusions
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