Parallel Diversity-Controlled Quantum-Behaved Particle Swarm Optimization Algorithm

In order to escape from premature convergence and improve the efficiency of the Quantum-behaved particle swarm optimization (QPSO) algorithm, this paper propose a new algorithm PDCQPSO, which employing diversity-controlled mechanism into QPSO to increase the diversity of population and parallel technique to shorten the running time of algorithm. A comprehensive experimental study is conducted on a set of benchmark functions, Comparison results show that PDCQPSO obtains a promising performance and less time cost on the majority of the test problems.