Continuous quantum ant colony optimization and its application to optimization and analysis of induction motor structure

A new kind of quantum ant colony algorithm for continuous space optimization is proposed in this paper, by the introduction of quantum computation into ant colony optimization. Each ant carries a group of quantum bits representing its current position. And quantum bits are updated by quantum rotation gate to make ant's position changed. Some quantum bits are mutated by quantum non-gate to increase the population diversity. Numerical simulation results show that new algorithm has better global search capability and faster convergence rate than classical ant colony optimization. Furthermore, the new algorithm is applied to optimization for motor structure parameters successfully, and satisfactory optimization results are obtained. A new effective method for motor structure optimization has been suggested based on continuous quantum ant colony optimization.