赵延军, 曲毅, 冯国旗. 粉煤灰颗粒分布重建果蝇算法分析[J]. 煤矿安全, 2018, 49(2): 163-165.
    引用本文: 赵延军, 曲毅, 冯国旗. 粉煤灰颗粒分布重建果蝇算法分析[J]. 煤矿安全, 2018, 49(2): 163-165.
    ZHAO Yanjun, QU Yi, FENG Guoqi. Fruit Flies Optimization Algorithm Analysis for Fly Ash Particle Size Distribution Reconstruction[J]. Safety in Coal Mines, 2018, 49(2): 163-165.
    Citation: ZHAO Yanjun, QU Yi, FENG Guoqi. Fruit Flies Optimization Algorithm Analysis for Fly Ash Particle Size Distribution Reconstruction[J]. Safety in Coal Mines, 2018, 49(2): 163-165.

    粉煤灰颗粒分布重建果蝇算法分析

    Fruit Flies Optimization Algorithm Analysis for Fly Ash Particle Size Distribution Reconstruction

    • 摘要: 为了适应工业场合粉煤灰颗粒粒径的快速检测,对果蝇优化算法粉煤灰颗粒粒径分布重建进行研究。搭建了颗粒粒径在线测量平台,选用标准粉煤灰颗粒为实验材料,在无噪声环境下进行实验模拟。采用果蝇算法反演待测粉煤灰颗粒粒径并将得到的结果与遗传算法和粒子群算法反演得到的结果进行比较。结论表明:采用果蝇优化算法反演颗粒粒径,相对误差为1.07%,运算时间为1.21 s,相比于另外2种算法,具有较好的反演精度和较快的迭代速度。

       

      Abstract: In order to adapt to the rapid detection of particle size of fly ash in industrial field, the fly ash particle size distribution reconstruction by fruit flies optimization algorithm was studied. An online measurement platform for particle size is set up. The standard fly ash particles were used as experimental materials to simulate the experiment under noisy environment. The particle size of the fly ash particle was calculated by fruit flies algorithm and the results were compared with those obtained by genetic algorithm and particle swarm algorithm. The results show that the particle size of the particle is 1.07% and the calculation time is 1.21 s by fruit flies optimization algorithm. Compared with the other two algorithms, it has good inversion accuracy and fast iteration speed.

       

    /

    返回文章
    返回