基于混合人工蜂群与泛化模式搜索算法的颗粒粒径分布重建

Reconstruction of Particle Size Distribution Based on Hybrid Artificial Bee Colony Algorithm and Generalized Pattern Search Algorithm

在光散射颗粒粒径分布测量过程中会产生大量测量数据,且无法对所有测量数据进行精确分析,进而降低了颗粒粒径分布重建的速度与精度,导致煤矿粉尘测量结果产生误差,出现安全隐患,危及生产和工作人员的安全。研究快速有效的反演算法,用于颗粒粒径分布的重建,成为颗粒粒径测量领域的重要课题之一。通过运用人工蜂群算法与泛化模式搜索算法相结合的混合算法,反演重建单峰R-R分布。将算法与遗传算法、Phillips-Twomey方法比较,结果表明,混合算法能够有效用于单峰R-R分布的反演重建,为工业应用提供有效且可靠的方法。

Large amount of measured data will be produced during the measurement process of light scattering particle size distribution and there is not a method could be utilized to get accurate analysis of all measured data,thereby,the accuracy and speed of the distribution reconstruction will be reduced,which could cause errors occurring in the measurement result of the coal dust and some hidden dangers that might endanger the safety of production and the staff. The inversion algorithm for the reconstruction of particle size distribution has become one of the important topics in the field of particle size measurement. This paper combined the artificial bee colony algorithm with generalized pattern search algorithm to reconstruct unimodal R-R distribution. Comparing this method with genetic algorithm and Phillips-Twomey method to get a result,it shows that the hybrid algorithm can be effectively applied to the inversion of unimodal R-R distribution reconstruction and can provide effective and reliable method for industrial applications.