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Central force optimisation (CFO) is a new deterministic multi-dimensional search evolutionary algorithm (EA) inspired by gravitational kinematics. CFO is a simple technique that is still in its infancy. This study evaluates CFO's performance and provides further examples of its effectiveness by applying it to a set of 'real-world' antenna benchmarks and to pattern synthesis for linear and circular array antennas. A new selection scheme is introduced that enhances CFO's global search ability while maintaining its simplicity. The improved CFO algorithm is applied to the design of a circular array with very good results. CFO's performance on the antenna benchmarks and the synthesis problems is compared to that of other EAs.