郑建国, 钱洁. 采用灰色码观测的量子进化算法[J]. 信息与控制, 2012, (3): 350-355. DOI: 10.3724/SP.J.1219.2012.00350
引用本文: 郑建国, 钱洁. 采用灰色码观测的量子进化算法[J]. 信息与控制, 2012, (3): 350-355. DOI: 10.3724/SP.J.1219.2012.00350
ZHENG Jianguo, QIAN Jie. A Quantum-inspired Evolutionary Algorithm Based on Gray Coding Observation[J]. INFORMATION AND CONTROL, 2012, (3): 350-355. DOI: 10.3724/SP.J.1219.2012.00350
Citation: ZHENG Jianguo, QIAN Jie. A Quantum-inspired Evolutionary Algorithm Based on Gray Coding Observation[J]. INFORMATION AND CONTROL, 2012, (3): 350-355. DOI: 10.3724/SP.J.1219.2012.00350

采用灰色码观测的量子进化算法

A Quantum-inspired Evolutionary Algorithm Based on Gray Coding Observation

  • 摘要: 针对传统量子进化算法采用二进制观测机制,导致量子波动幅度较大且连续观测到相邻实数概率低的问题, 本文提出了一种采用灰色码观测机制的量子进化算法.由于量子擅长全局搜索,灰色码擅长局部搜索,因此所提出的算法能较好平衡勘探和开采能力, 量子进化更加平滑和高效.通过实验表明,算法能有效避免早熟和局部极值等问题,算法的精度更高,收敛速度更快.

     

    Abstract: A quantum-inspired evolutionary algorithm (QEA) based on gray code observation is proposed to overcome the large fluctuation range of quantum and low probability of continuous observation of nearby real number with binary observation mechanism in traditional QEA. Since quantum is good at global searching and gray code is good at local searching, the proposed algorithm can well balance exploration and exploitation. It can make the evolution of quantum more smooth and more efficient. Experiments show that the algorithm can avoid premature and local extreme. Meanwhile, the accuracy and convergence speed are improved.

     

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