Continuous quantum immune clonal optimization and its application to calculation and analysis of electromagnetic in induction motor

A new kind of quantum immune clonal algorithm for continuous space optimization is proposed in this paper, by the introduction of quantum computation into immune clonal optimization. The antibodies are represented by a group of quantum bits. Quantum bits are updated by quantum rotation gate to accelerate convergence. And some quantum bits are mutated by quantum non-gate to avoid premature convergence. Numerical simulation results show that new algorithm has better global search capability and faster convergence rate than classical immune clonal algorithm and genetic algorithm. Furthermore, the new algorithm is successfully applied to motor structure parameters optimization, and satisfactory optimization results are obtained. A new effective method for motor structure optimization has been suggested based on continuous quantum immune clonal optimization.