Decomposition-based evolutionary multi-objective optimization approach to the design of concentric circular antenna arrays

Abstract

We investigate the design of Concentric Circular Antenna Arrays (CCAAs) with λ2 uniform inter-element spacing, non-uniform radial separation, and non-uniform excitation across di®erent rings, from the perspective of Multi-objective Optimization (MO). Unlike the existing single-objective design approaches that try to minimize a weighted sum of the design objectives like Side Lobe Level (SLL) and principal lobe Beam-Width (BW), we treat these two objectives individually and use Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) with Di®erential Evolution (DE), called MOEA/D-DE, to achieve the best tradeo® between the two objectives. Unlike the single-objective approaches, the MO approach provides greater flexibility in the design by yielding a set of equivalent final (non-dominated) solutions, from which the user can choose one that attains a suitable trade-o® margin as per requirements. We illustrate that the best compromise solution attained by MOEA/D-DE can comfortably outperform state-of-the-art variants of single-objective algorithms like Particle Swarm Optimization (PSO) and Di®erential Evolution. In addition, we compared the results obtained by MOEA/D-DE with those obtained by one of the most widely used MO algorithm called NSGA-2 and a multi-objective DE variant, on the basis of the R-indicator, hypervolume indicator, and quality of the best trade-o® solutions obtained. Our simulation results clearly indicate the superiority of the design based on MOEA/D-DE.