The development of real-coded quantum clonal selection algorithm for the optimization of complex functions and interval type-2 fuzzy logic controller

This work presents the real coded quantum clonal selection algorithm (RCQCSA) to solve optimization problems. The algorithm combines the traditional clonal selection algorithm with the real coded quantum evolutionary algorithm (RCQEA). A new quantum rotation gate (QRG) is proposed to replace the inefficient quantum rotation gate used in RCQEA. In addition, reducing the search space technique, which is implemented based on the evolutionary process of the algorithm, is adopted to improve the convergence rate of the algorithm. Thanks to the running mechanism of the algorithm, the proposed QRG and the reducing search space technique, the convergence rate of the algorithm is improved. The algorithm is used in the optimization of two different problems. It was first used in the optimization of high dimensional continuous optimization problems, then in the optimization of interval type 2 fuzzy logic controller that controls the inverted pendulum system. As the performance criteria of the optimization algorithms, the number of iterations in which the algorithms reach the global optimum solutions and their stability in optimization are used. The simulation results show that the RCQCSA has the characteristics of rapid convergence compared to other algorithms in the optimization of continuous optimization problems and the interval type-2 fuzzy logic controller, and has good global search capability and stability.