01557nas a2200193   4500008004100000245013800041210006900179260000900248300001400257490000700271520080600278653002201084653004101106653004701147653002501194100001301219700001401232856011701246       2013                        eng d00aParameters selection in the discrete particle swarm optimization algorithm solving gate and runway combinational optimization problem0 aParameters selection in the discrete particle swarm optimization  c2013  a197 - 2010 v113 aGate 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.10aAirport operation10aDiscrete particle swarm optimization10aGate and runway combinational optimization10aParameters selection1 aDing, J.1 aZhang, Y.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84880782704&partnerID=40&md5=b89c7c4e42430962155e9283216c7b7001744nas a2200241   4500008004100000245009300041210006900134260000900203300001400212490000700226520097400233653002601207653002301233653001901256653002901275653001901304100001201323700001301335700001201348700001201360700001301372856011701385       2012                        eng d00aA novel RDB-SW approach for commodities price dynamic trend analysis based on web mining0 anovel RDBSW approach for commodities price dynamic trend analysi  c2012  a230 - 2350 v103 aIn 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.10aDichotomy backfilling10aDisturbance factor10aImperfect data10aPrice forecast algorithm10aSliding window1 aZhu, Q.1 aZhou, P.1 aCao, S.1 aYan, Y.1 aDing, J.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84866463450&partnerID=40&md5=5874dc4739d723f46728438c22a52b9a