@article {403,
	title = {Parameters selection in the discrete particle swarm optimization algorithm solving gate and runway combinational optimization problem},
	journal = {Journal of Digital Information Management},
	volume = {11},
	year = {2013},
	note = {Export Date: 10 July 2014},
	month = {2013},
	pages = {197 - 201},
	abstract = {Gate and runway combinational optimization (GRCO) problem is of great significance in airport operation. In this paper, experimental analysis is performed on the parameters characteristics of the discrete particle swarm optimization (DPSO) algorithm for combinatorial optimization problems. Inertia weight, acceleration constants and population size all have an important impact on the performance of the algorithm. Optimal values exist in the specific application of the respective parameters. The optimal value interval of the population size does not vary with different acceleration constants when inertia weight is constant. Increasing the population size can improve the solution quality, but the time overhead increases significantly. Finally, each parameters selection guidelines are provided.},
	keywords = {Airport operation, Discrete particle swarm optimization, Gate and runway combinational optimization, Parameters selection},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84880782704\&partnerID=40\&md5=b89c7c4e42430962155e9283216c7b70},
	author = {Ding, J. and Zhang, Y.}
}
@article {338,
	title = {A novel RDB-SW approach for commodities price dynamic trend analysis based on web mining},
	journal = {Journal of Digital Information Management},
	volume = {10},
	year = {2012},
	note = {Cited By (since 1996):5Export Date: 10 July 2014},
	month = {2012},
	pages = {230 - 235},
	abstract = {In order to improve the accuracy of price forecasting for dynamic trend analysis on imperfect data by web extracting, a novel repair data algorithm based on dichotomy backfilling is proposed in this paper. The price forecasting algorithm based on the Sliding Window (SW) is utilized to verify the validity of Repair Dichotomy Backfilling (RDB) algorithm. Experiments demonstrated that the mean absolute errors can be achieved to 5.31 percent average. Furthermore, the price forecasting algorithm based on original data and that based on RDB data are explored to compare their accuracy. Experiments demonstrated the reduction of mean absolute errors in the price dynamic trend analysis verification model. The average MAE on original data is 2.38 percent when the average MAE on RDB data is 2.31 percent. Experiment results proved that the proposed RDB-SW approach is meaningful and useful to analyze and to research the price market on imperfect data by Web extracting.},
	keywords = {Dichotomy backfilling, Disturbance factor, Imperfect data, Price forecast algorithm, Sliding window},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84866463450\&partnerID=40\&md5=5874dc4739d723f46728438c22a52b9a},
	author = {Zhu, Q. and Zhou, P. and Cao, S. and Yan, Y. and Ding, J.}
}
