Quantum crossover based quantum genetic algorithm for solving non-linear programming

Quantum computing proved good results and performance when applied to solving optimization problems. This paper proposes a quantum crossover-based quantum genetic algorithm (QXQGA) for solving non-linear programming. Due to the significant role of mutation function on the QXQGA's quality, a number of quantum crossover and quantum mutation operators are presented for improving the capabilities of searching, overcoming premature convergence, and keeping diversity of population. For calibrating the QXQGA, the quantum crossover and mutation operators are evaluated using relative percentage deviation for selecting the best combination. In addition, a set of non-linear problems is used as benchmark functions to illustrate the effectiveness of optimizing the complexities with different dimensions, and the performance of the proposed QXQGA algorithm is compared with the quantum inspired evolutionary algorithm to demonstrate its superiority.