The paper describes a novel approach to the design of non-uniform planar circular antenna arrays for achieving maximal side lobe level suppression and directivity. The current excitation amplitudes and phase perturbations of the array elements are determined using an Adaptive Memetic algorithm resulting from a synergy of Differential Evolution (DE) and Learning Automata that is able to significantly outperform existing state-of-the-art approaches to the design problem. However, existing literature considers the design problem as a single-objective optimization task that is formulated as a linear sum of all the performance metrics. Due to the conflicting nature of the various design objectives, improvements in a certain design measure causes deterioration of the other measures. Following this observation, the single-objective design problem is reformulated as a constrained multi-objective optimization task. The proposed memetic algorithm is extended to the multi-objective framework to generate a set of nondominated solutions from which the best compromise solution is selected employing a fuzzy membership based approach. An instantiation of the design problem clearly depicts that the multi-objective approach provides simultaneous side lobe level suppression and directivity maximization in comparison to the single-objective scenario.
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