Adaptive quantum-behaved particle swarm optimization algorithm based on cloud model

Utilizing the characteristic of cloud model principles which can make good balance between the randomness and the fuzziness, an adaptive quantum-behaved particle swarm optimization algorithm based on cloud model is proposed. Firstly, the control mechanism of quantum-behaved particle swarm optimization algorithm is analyzed. On this basis, the absorption-expansion factor of each particle is adaptively controlled by cloud operators to achieve the dynamic adjustment to the positions of particles in evolutionary process. Thus, the proposed algorithm obtains a higher convergence speed and a stronger global search ability. Programs are modified for the targeted optimization to make the proposed algorithm effectively avoid falling into local optimum. The results of simulation experiments with typical test functions show that the proposed algorithm has advantages in search ability, accuracy and stability, and it is more effective than other similar algorithms.