Decomposition-Based Evolutionary Multi-Objective Optimization Approach to the Design of Concentric Circular Antenna Arrays

Abstract

Abstract-We investigate the design of Concentric Circular Antenna Arrays (CCAAs) with λ/2 uniform inter-element spacing, non-uniform radial separation, and non-uniform excitation across different 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 Differential Evolution (DE), called MOEA/D-DE, to achieve the best tradeoff 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 (nondominated) solutions, from which the user can choose one that attains a suitable trade-off 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 Differential 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 Rindicator, hypervolume indicator, and quality of the best tradeoff solutions obtained. Our simulation results clearly indicate the superiority of the design based on MOEA/D-DE.

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