Antenna benchmark performance and array synthesis using central force optimisation

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.