CAJ | 학술논문

利用云模型理论能兼顾随机性和模糊性的特质，提出一种基于云模型的自适应量子粒子群优化算法。首先分析量子粒子群算法的控制机制，在此基础上，使用云算子实现对每个粒子的吸收扩张因子自适应控制，达到在进化过程中对粒子飞行位置动态调整的目的，使算法具有较快的收敛速度和较强的全局搜索能力。同时，补充针对性的优化方案，有效避免算法陷入局部最优。对典型测试函数的仿真对比实验表明，该算法具有寻优能力强、搜索精度高、稳定度好等优点，相比其它同类算法具有一定优势。
이용운모형이론능겸고수궤성화모호성적특질，제출일충기우운모형적자괄응양자입자군우화산법。수선분석양자입자군산법적공제궤제，재차기출상，사용운산자실현대매개입자적흡수확장인자자괄응공제，체도재진화과정중대입자비행위치동태조정적목적，사산법구유교쾌적수렴속도화교강적전국수색능력。동시，보충침대성적우화방안，유효피면산법함입국부최우。대전형측시함수적방진대비실험표명，해산법구유심우능력강、수색정도고、은정도호등우점，상비기타동류산법구유일정우세。
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.