Abstract: In order to improve the search ability of traditional teaching-learning optimization algorithm, the corresponding quantum version of the algorithm is proposed by integrating principle of quantum computing. In proposed method, two learning mechanisms, teacher self-study and student learning from teacher, are used to search the global optimal solution. The individual uses qubits coding, and the search process is performed on the Bloch sphere. The individual is updated by the rotation of the qubits about axis, and then is decoded into the Bloch spherical coordinates of the qubits. Because the proposed method extends the search of each dimension in the common teaching-learning algorithm to the Bloch sphere, the search process can be more refined, thus enhancing the traversal effect on the solution space. The experimental results show that the optimization ability of this method is not only better than that of common teaching-learning-based optimization algorithm, but also better than that of other classical swarm intelligence optimization algorithms. The results show that the combination of some mechanisms of quantum computing and intelligent optimization can improve its optimization performance.

Key words: intelligent optimization algorithm, quantum computing, teaching-learning-based optimization algorithm, quantum teaching-learning-based optimization, function optimization