A new quantum chaos particle swarm optimization (QCPSO) was proposed to accurately estimate the uncertain parameters of the fractional order hyper chaotic system. The QCPSO algorithm was realized by analyzing the mechanism of quantum behaved particle swarm optimization (QPSO) and combining the correlation between quantum entanglement and chaotic system. Firstly

the center of potential well was replaced by a fixed point of chaotic attractor. The particles which outside the attractor were gradually converged to the attractor

and the particles which inside the attractor were quickly diffused. Secondly

in order to guarantee the diversity of the initial value of the chaotic particles

the particle update mechanism based on random mapping was proposed. Finally

a scale adaptive strategy was proposed to solve the problem of search stagnation of the algorithm. The parameters of fractional order hyper chaotic system were estimated by the QCPSO algorithm

and the results showed that the QCPSO algorithm has faster convergence speed and higher accuracy than improved differential evolution algorithm

adaptive artificial bee colony algorithm and improved QPSO algorithm.