A Quantum Behaved Particle Swarm Optimization with a Chaotic Operator

As a popular population based Evolutionary Algorithm, quantum behaved particle swarm optimization (QPSO) has applied widely in many real-world problems. In this paper, for further enhancing the performance of QPSO, we proposed a popular chaotic map into it. The new chaotic operator not only accelerate the convergence rate but also strengthen the search ability in the total space of the original QPSO. Furthermore, we verify the revised algorithm on some traditional benchmark functions. The final compared results on the images prove the superior of our algorithm.