Quantum inspired evolutionary algorithms with parametric analysis

Quantum inspired evolutionary algorithms are heuristic search methods, where all individuals in the search space directed to the best solution position. Using quantum gate operator with other evolutionary operators such as selection, crossover and mutation constitute a challenge in terms of their types and their parameters. In this paper we design several quantum crossover and quantum mutation operators with different parameters, the contribution of each operator to the success of our proposed algorithm analyzed via relative percentage deviation method. The proposed work gives a decision whether to use selection operator or not, it uses catastrophe operator to overcome local minima. The experimental results demonstrate the superiority of the proposed approach to solve non-linear programming problems.