A quantum-inspired evolutionary clustering algorithm

Inspired by the concept and principles of quantum computing, the classical quantum-inspired evolutionary algorithm (QEA) provides a useful way to find out the approximate solution of many optimization problems. However, compared with other heuristic algorithms, the slow convergence speed of QEA has been an important issue when it is applied to solve the optimization problems. As such, an improved version, called fast quantum-inspired evolutionary algorithm (FQEA), is proposed in this paper. By adding a fast repair facility, the proposed algorithm can not only accelerate the convergence speed of the search process of QEA, it can also provide a better result than QEA. Experimental results show that the proposed algorithm FQEA can provide a better result than those obtained by QEA and k-means algorithm.