COMPUTER SYSTEMS APPLICATIONS
1003-3254
1
7
10.15888/j.cnki.csa.008659
article
基于M-ACO算法的空气污染源反演
Retrieval of Air Pollution Sources Based on M-ACO Algorithm
为了解决空气污染源反演的盲目性和低效率问题, 本文提出了一种基于改进型蚁群算法(modified-ant colony optimization, M-ACO)的空气污染源反演方法. 利用点源高斯扩散模型建立污染源反演模型, 采取蚁群算法(ant colony optimization, ACO)来求解. 针对蚁群算法中存在的缺点, 引入遗传算法的选择交叉思想, 从而丰富种群的多样性来避免陷入局部极值; 同时设计奖惩因子机制, 对信息素更新规则进行改进来使算法更快地收敛, 进而归纳为M-ACO算法. 通过对比实验, 证明了M-ACO算法相比于传统ACO算法来说, 能够使得污染源的反演结果更准确和高效, 为空气污染源反演的实际应用提供了有效的理论支撑.
To address the blindness and low efficiency in the inversion of air pollution sources, this study proposes a novel method for air pollution source inversion based on a modified ant colony optimization (M-ACO) algorithm. The Gaussian diffusion model for point source pollution is used to establish the pollution source inversion model which is solved by an ant colony optimization (ACO) algorithm. In view of the shortcomings in the ACO algorithm, the idea of selection and crossover in genetic algorithms is introduced to enrich population diversity, which thus avoids falling into local extrema. At the same time, a reward and punishment factor mechanism is designed to improve the pheromone update rule so that the algorithm can converge faster. It is then summarized as the M-ACO algorithm. Comparative experiments prove that the M-ACO algorithm can make the inversion results of pollution sources more accurate and efficient than the ACO algorithm. It provides effective theoretical support for the practical application of air pollution source inversion.
空气污染;高斯扩散模型;改进型蚁群算法;污染源反演;选择交叉;奖惩因子
air pollution;Gaussian diffusion model;modified-ant colony optimization (M-ACO) algorithm;pollution source inversion;selection and crossover;reward and punishment factor
祁柏林,崔英杰,王帅,武暕
QI Bo-Lin, CUI Ying-Jie, WANG Shuai, WU Jian
csa/article/abstract/8659