An Improved Quantum Crossover Operator for Binary Evolutionary Optimization of Thinned Array Antennas

Many engineering optimization problems may be rephrased in terms of equivalent binary problems, and these can be effectively tackled with evolutionary algorithms. Unfortunately, the fitness function computation may be extremely timeconsuming when dealing with antenna designs. Therefore, it is of paramount importance to speed up the convergency and to improve the performance of this kind of algorithm. The recent introduction and the increasing availability of quantum computing may be very effective in accelerating the design process, even though new approaches and algorithms are needed in order to exploit the specificity of these instruments. In this paper, a new version of a novel quantum crossover operator for binary Genetic Algorithm (bGA) has been introduced and compared with its previous version. They have been successfully tested on different mathematical benchmark functions and on a preliminary thinned array design.