Research Article The Evaluation of Dynamic Airport Competitiveness Based on IDCQGA-BP Algorithm

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1 Mathematcal Problems n Engneerng Volume 2013, Artcle ID , 8 pages Research Artcle The Evaluaton of Dynamc Arport Compettveness Based on IDCQGA-BP Algorthm Qang Cu, 1 Ha-bo Kuang, 1 and Ye L 2 1 Transportaton Management College, Dalan Martme Unversty, o. 1 Lngha Road, Dalan , Chna 2 Faculty of Management and Economcs, Dalan Unversty of Technology, o. 2 Lnggong Road, Dalan , Chna Correspondence should be addressed to Qang Cu; cuqang1011@163.com Receved 25 August 2013; Accepted 30 ovember 2013 Academc Edtor: Daoy Dong Copyrght 2013 Qang Cu et al. Ths s an open access artcle dstrbuted under the Creatve Commons Attrbuton Lcense, whch permts unrestrcted use, dstrbuton, and reproducton n any medum, provded the orgnal work s properly cted. Amed at the multdmensonal and complex characterstc of arport compettveness, a new algorthm s proposed n whch BP neural network s optmzed by mproved double chans quantum genetc algorthm (IDCQGA-BP). The new algorthm s better than exstng algorthms n convergence and the dversty of quantum chromosomes. The emprcal data of eght arports n Yangtze Rver Delta n 2011 and 2012 s appled to verfy the feasblty of the new algorthm, and then the compettveness of the eght arports from 2013 to 2015 s gotten through the algorthm. The results show the followng. (1) The new algorthm s better than the exstng optmzaton algorthms n the aspects of error accuracy and run tme. (2) The gaps of the arports n Yangtze Rver Delta are narrowng; the competton and cooperaton are gettng stronger and stronger. (3) The man ncrease reason of arport compettveness s the ncrease of own nvestment. 1. Introducton In recent years, wth the rapd development of Chna and the mprovement of household consumpton level, avaton demandhasncreasedenormouslynchna.manyctesare plannng to nvest on new arports or on arport extenson, whch can offer convenence to people s travel and promote the development of Chnese economy. However, what about the compettveness of Chnese arports over the years? It has become the center of publc concern. For the past few years, the evaluaton of arport compettveness has been a popular research topc. Leshout and Matsumoto [1] use route choose model to analyze the probable reconstructs compettveness of Tokyo Haneda Arport. The results show that Haneda Arport s market share n Japan and Asa-Pacfc regon wll ncrease sgnallywththehelpofjapanesegovernment.park[2] uses fuzzy lngustc approach to evaluate eght man arports n East Asa from the aspects of geographcal characterstcs, access system, envronmental effects, operatonal condtons of lnes, regonal development, avalablty of plannng mplementaton, socoeconomc effects, and arport charges. Lee and Yang [3] analyze the strategy of Incheon Arport to be the logstc center of ortheast Asa; the results show that the key s whether Incheon Arport could attract suffcent global logstcservce supplers. Park [4] bulds ndex system from the aspects of servce, demand, management, faclty and regonal space, then evaluates the statc compettveness of the man arports n East Asa through multlayer decson model. Yeh et al. [5] buld ndex system from the aspects of operatonal management, arport faclty and consumer servce qualty, then analyze the statc compettveness of eleven Asa-Pacfc arports through fuzzy multple attrbute evaluaton method. Peng and Zhan [6] calculate the statc arport compettveness of Hangzhou arport and other Asa- Pacfc arports through AHP model. X. Y. Wu and K. Y. Wu [7] establshe AHP evaluaton system and compare the advantages and dsadvantages of fve bg arports n Asa- Pacfc Ocean and then gve out some mprovement measures for Pudong arport n Shangha. The dsadvantages of exstng research are as follows. (1) The exstng research manly focuses on the evaluaton of statc arport compettveness; the arport compettveness under competton and cooperaton has not been

2 2 Mathematcal Problems n Engneerng Table 1: The evaluaton ndex system of dynamc arport compettveness. Index classfcaton Frst-class ndces Second-class ndces The proporton of busness ncome dvded by cty thrd ndustry Regonal nfluence output V 1 Pad tax V 2 (hundred mllon yuans) Flght zone level V 1 3 Index system of dynamc arport compettveness Own strength Market condton The proporton of staff wth college degree or above V 4 Total assets V 5 (hundred mllon yuans) Investment amount V 6 (hundred mllon yuans) The number of nonexclusve servce desks V 7 Cty resdent transportaton expense V 8 Average arcrafts movement n a day V 9 (sortes/day) Servce radus V 2 10 Freght throughput V 11 (ten thousand tons) Passenger throughput V 12 (ten thousand person-tme) evaluated, whch leads to the dfference between evaluated compettveness and real compettveness. (2) Few peces of lterature have consdered the error accuracy of evaluaton method n the dfference between evaluated compettveness and real compettveness. There are lnear features and nonlnear features between arport compettveness and ts evaluaton ndces. However, few peces of lterature have consdered nonlnear features, whch may result n rratonal results. Ths paper s structured as follows: frstly, the evaluaton ndex system of arport compettveness s bult; secondly, a new algorthm s proposed n whch BP neural network s optmzed by mproved double chans quantum genetc algorthm (IDCQGA-BP); Thrdly, the real data of 8 Chnese arports n Yangtze Rver Delta regon s appled to do the emprcal study. Moreover, the advantages of the new algorthm over other evaluaton methods are analyzed from the aspects of error precson and runnng tme. Fnally, the eght arports dynamc competveness from 2013 to 2015 s calculated through the new algorthm. The results show that the new algorthm has good applcablty. 2. Index System Accordng to the exstng lterature [8, 9], the evaluaton ndex system comes from three aspects: regonal nfluence, own strength, and market condton. The ndex system s shown n Table 1. otes. (1) Flght zone level s defned as the bggest arcraft that the facltes of arport flght zone can support; t has two measurement ndcators: the length of runway and the dstance between the wngspan of the bggest supported arplane and the felly of man landng gear. In Chna, flght zone level s dvded nto several grades: 4F, 4E 60, 4E 45, 4D, and4c, and ther measurable values are 5, 4.5, 4, 3, and 2, respectvely [9]. (2) Servce radus s offcally defned by Cvl Avaton Admnstraton of Chna as people amount wthn 100 km from arport or wthn 1.5 hours drvng range. Servce w 11 ζ m W m1 W m2 W mp τ 1 τ 2 τ P ξ 1 ξ 2 ξ w P Output layer Hdden layer Input layer Fgure1:ThestructureofBPneuralnetwork. radus s defned as people amount of the drectly controlled muncpaltes and prefecture level ctes n Chna [9]. Accordng to the results of famous scholars and nsttutes [10, 11], the result-type measurement ndces of compettveness are market share and resource usage rate. So ths paper defnes man busness ncome and return on asset as the deal outputs. 3. The Model 3.1. BP eural etwork. The basc structure of BP neural network [12]sshownnFgure1. If the non-lnear smooth actvaton functon s defned as g:r 1 R 1, the weght matrxes are defned as W 0 = {W 0 mp } M P and ω 0 = {ω 0 pn } 1 p P,1 n ; foranynputξ = (ξ 1,...,ξ ) R,therealoutputs ς 0 m =g(w0 m τ0 )=g( W 0 mp τ0 p ), P p=1 The output of hdden layer s τ 0 P =g(ω0 p ξ)=g( ω 0 pn ξ n), n=1 m=1,2,...,m. p=1,2,...,p. (1) (2)

3 Mathematcal Problems n Engneerng 3 If the nput s {ξ j } J j=1 R, ts deal output s {o m } M m=1 R M and ts real output of BP neural network s {ς m } M m=1 RM ; theerrorfunctoncanbedefnedas E (W, ω) = 1 2 M o m ς m 2. (3) m=1 s European orm. Gradent descent algorthm (GDA) s the common method to tran BP neural network. However, tradtonal gradent descent algorthm has some dsadvantages n convergng slowly and fallng nto the local mnmum pont easly [13]. Two methods are appled to mprove GDA. (1) Gradent descent algorthm wth momentums and adaptve varable rate: adaptve varable rate can accelerate the algorthm convergence, and momentums can reduce the shock and seek global optmum. (2) Some ntellgent optmzaton algorthms can optmze the weghts and threshold of BP neural network, whch can accelerate the algorthm convergence and seek global optmum. The ntellgent optmzaton algorthms contan genetc algorthm, partcle swarm optmzaton, and so on The Evaluaton Algorthm of Dynamc Arport Compettveness Based on IDCQGA-BP. Quantum genetc algorthm (QGA) [14, 15] s a probablty optmzaton algorthm based on the prncple of quantum computng. It has been wdely appled n combnaton optmzaton and functon optmzaton. Double chans quantum genetc algorthm (DCQGA) s one knd of QGA and has been a popular research topc [16]. In DCQGA, the ntal value of θ j of the quantum bt s probablty ampltude [cos θ j, sn θ j ] T s generated randomly n (0, 2π), = 1,2,...,n, j = 1, 2,...,m. n stands for populaton sze; m stands for the number of quantum bts n each quantum chromosome. The phase s updated by quantum rotaton gate and chromosomal mutaton s realzed by quantum nongate, whch can enlarge the search space of quantum chromosome and accelerate the algorthm convergence. Accordng to lterature [16 18], ths paper proposes a new algorthm n whch BP neural network s optmzed by mproved double chans quantum genetc algorthm (IDCQGA-BP). The new algorthm calculates the selectve probablty and the expectaton reproducton rate of quantum chromosome through vector dstance. The new algorthm can evaluate the dynamc arport compettveness under competton and cooperaton. Its prncple s that IDCQGA- BP algorthm calculates the exstng arport compettveness through exstng data, and then the nfluence of exstng compettveness on the future compettveness s evaluaton ndcessanalyzedfromtheaspectsofcompettonand cooperaton. The future value of evaluaton ndces s gotten accordng to the nfluence, and then IDCQGA-BP s used to calculate the future arport compettveness based on the future value of evaluaton ndces. The steps of the algorthm are as follows. (1) Intalze the quantum populaton. Choose n quantum chromosomes wth m quantum bts n (0, π/2),whchform the ntal quantum populaton X = {x 1,x 2,...,x n }.The probablty ampltude of each quantum bt s consdered as two paratactc genes and each chromosome contans two paratactc gene chans; each gene chan stands for an optmal soluton: cos θ x = 1 cos θ 2 sn θ 1 sn θ cos θ m sn θ, (4) m θ j =π/2 rand, where rand s the random number n (0, 1). (2) Transform the soluton space. In ntal populaton, each quantum chromosome contans 2m quantum bts probablty ampltude. hs step, lnear transformaton s appled to map the 2m quantum bts probablty ampltude n (0, π/2) m to the weght value space of BP neural network. Accordng to lterature [19],the ntalvalue of the weghts n BP neural network should be the random value n [a, b].ifthe jth quantum bt of quantum chromosome x s [α j,β j ]T,ts correspondng value space vector s X jc =a+(b a) α j, (5) X js =a+(b a) β j, where X jc s the cosne soluton of the jth quantum bt of quantum chromosome x, X js sthesnesolutonofthejth quantum bt of quantum chromosome x, α j s the probablty ampltude of X jc correspondng to quantum state 0, andβ j stheprobabltyampltudeofx jc correspondng to quantum state 1. (3) Calculate the vector dstance and calculate the selectve probablty and the expectaton reproducton rate of populaton X basedonvectordstanceconcentraton. The vector dstance of chromosome x s n ds (x ) = F (x ) F(x k ). (6) k=1 The vector dstance concentraton s 1 C(x )= ds (x ) = 1 n k=1 F(x. (7) ) F(x k ) The selectve probablty s ds (x P(x )= ) n ds (x ) = n k=1 F(x ) F(x k ) n n k=1 F(x. (8) ) F(x k ) The expectaton reproducton rate s n e(x )= F(x ) C(x ) =F(x ) F(x ) F(x k ). (9) (4) Sort thentalpopulatonx accordng to the selectve probablty P and the expectaton reproducton rate e. The top ten quantum chromosomes that are hgh n expectaton reproducton rate enter nto new populaton X new and the h quantum chromosomes whose selectve probablty s bgger k=1

4 4 Mathematcal Problems n Engneerng than certan value P r (P r = 0.6 max(p)) enterntonew populaton X new.thenn 10 hquantum chromosomes generated randomly n (0, π/2) enter nto new populaton X new. (5) Update the quantum phase. The formula s cos (Δθ) sn (Δθ) (θ) (θ+δθ) [ ][cos ]=[cos ], (10) sn (Δθ) cos (Δθ) sn (θ) sn (θ+δθ) Δθ = sgn(sn(θ θ 0 )) Δθ 0 exp( ( F(x ) F mn )/( F max F mn )), where s the gradent functon; θ 0 s the teratve ntal value. (6) Calculate the mutaton. Quantum nongate s used to calculate the mutaton. Choose a chromosome randomly from the populaton accordng to the mutaton probablty; then choose several quantum bts randomly n the selected chromosome and use quantum nongate to process selected quantum bts, whch can exchange the two probablty ampltudes of the quantum bt and calculate the two gene chans mutaton. (7) Calculate the relevance degrees of the chromosomes. The relevance degree functon s based on the error functon of BP neural network. It s F(x )=exp ( 1 2 tar (x ) g(wg(ωx )) 2 ), (11) where tar s the deal output of BP neural network, W s the weght matrx between hdden layer and output layer, ω s the weght matrx between hdden layer and nput layer, and g s actvaton functon. (8) Revse the algorthm. Sort the quantum bt angel θ of all quantum chromosomes n X new and record the maxmum θ max and mnmum θ mn.if θ max θ mn <ε, then the value spaceshouldberesetas(0, π/2). (9) Return to step (2) and repeat the rest steps untl ether the convergence condton or the maxmum teraton step s satsfed. (10) Use the traned BP neural network and the data at t and t 1to calculate the arport compettveness Com(t) and Com(t 1). (11) ormalze the man busness ncome and return on assets at t and t 1and set ther average value as the deal output of BP neural network, whch can be used to calculate the error accuracy of the algorthm. (12) Analyze the nfluence of the arport compettveness at t on the evaluaton ndces at t+1andestmatethevalueof the ndces at t+1.therearetwoaspects.(1)competton. The key pont s to analyze the nfluence of exstng compettveness on the future market shares of ths arport and other arports. (2) Cooperaton. The key pont s to analyze the nfluence of the cooperaton on nternatonal lnes on the future market shares of ths arport and other arports. (1) Competton. Accordng to lterature [9], f arport A and arport B have drect competton (the servce raduses of the two arports have overlap), then the compettveness of arport A wll nfluence the future market condtons of arport A and arport B. he same way, the compettveness of arport B wll nfluence the future market condtons of arport A and arport B. If there are arports n a regon whose servce raduses have overlap and the compettveness of the arports at t s (Com 1 (t), Com 2 (t),..., Com (t)) and the compettveness of the arports at t 1s (Com 1 (t 1), Com 2 (t 1),..., Com (t 1)), the compettveness change of arport from t 1to t wll nfluence the market share of arport at t+1. The change value 1 of passenger throughput V 12 at t+1s ( Com (t) Com (t) Com (t 1) Com (t 1) )V 12 (t). (12) Smlarly, the change value 1 of freght throughput V 11,the change value 1 of average arcrafts movement n a day V 9,the change value 1 of busness ncome V 01,thechangevalue1of pad tax V 2, and the change value 1 of total assets V 5 are ( Com (t) Com (t) Com (t 1) Com (t 1) )V 11 (t), ( Com (t) Com (t) Com (t 1) Com (t 1) )V 9 (t), ( Com (t) Com (t) Com (t 1) Com (t 1) )V 01 (t), ( Com (t) Com (t) Com (t 1) Com (t 1) )V 2 (t), ( Com (t) Com (t) Com (t 1) Com (t 1) )V 5 (t). (13) (2) Cooperaton. In a regon, some arports are nternatonal hub arports, and some are regonal arports. The cooperaton among the arports s emboded n the cooperaton on nternatonal lnes between nternatonal hub arports and regonal arport. The nternatonal lnes of the arports at t are 1 (t), 2 (t),..., (t); thebggertheproporton of arport s nternatonal lnes s, the more cooperaton benefts arport can get. So the change value 2 of passenger throughput V 12 at t+1s V 12 (t) p t θ t, (14) where p t s the proporton of the passenger throughput of nternatonal lnes dvded by the total passenger throughput and θ t s the natural populaton growth rate of the regon. Smlarly, the change value 2 of freght throughput V 11, the change value 2 of average arcrafts movement n a day V 9,

5 Mathematcal Problems n Engneerng 5 the change value 2 of busness ncome V 01,thechangevalue 2ofpadtaxV 2, and the change value 2 of total assets V 5 are V 11 (t) p t θ t, V 9 (t) p t θ t, V 01 (t) p t θ t, V 2 (t) p t θ t, V 5 (t) p t θ t. (15) Accordng to the results of competton and cooperaton, the passenger throughput of arport at t+1s V 12 ( (t+1)) =V 12 (t) +( Com (t) Com (t) Com (t 1) Com (t 1) )V 12 (t) Table 2: The parameters of IDCQGA-BP. Input neurons 12 Hdden neurons 20 Output neurons 1 Weghts 260 Populaton sze 1000 Mutaton rate 0.1 Intal phase 0.01 π Target error 0.01 Max teratons 5000 [a, b] [0, 0.38] ε V 01 ( (t+1)) =V 01 (t) +( Com (t) Com (t) Com (t 1) Com (t 1) ) V 01 (t) + V 2 ( (t+1)) V 01 (t) p t θ t, =V 2 (t) +( Com (t) Com (t) Com (t 1) Com (t 1) ) V 2 (t) + V 2 (t) p t θ t, + V 12 (t) p t θ t. (16) V 5 ( (t+1)) =V 5 (t) +( Com (t) Com (t) Com (t 1) Com (t 1) ) The freght throughput V 11, the average arcrafts movement n a day V 9, the busness ncome V 01,thepadtaxV 2, and the total assets V 5 at t+1are V 5 (t) + V 5 (t) p t θ t. (17) V 11 ( (t+1)) =V 11 (t) +( Com (t) Com (t) Com (t 1) Com (t 1) ) V 11 (t) + V 9 ( (t+1)) V 11 (t) p t θ t, =V 9 (t) +( Com (t) Com (t) Com (t 1) Com (t 1) ) V 9 (t) + V 9 (t) p t, Then, accordng to ther hstorcal growth rates, ths paper predcts the future value of flght zone level, the proporton of staff wth college degree or above, nvestment amount, the number of nonexclusve servce desks, cty resdent transportaton expense, servce radus, and cty thrd ndustry output. (13) Set the ndces calculated n step (12) as the network nputs, and then use the BP neural network traned n step (10) to calculate the dynamc arport compettveness under competton and cooperaton at t+1. The man mprovements are emboded n the followng. (1) Thentalvalueofphasesrandomlyselectedn(0, π/2) nstead of (0, 2π) andthevaluespaceofθ s revsed at step (8). It can reduce the search space of the algorthm sgnally and assure the algorthm s faster convergence. (2) The selectve probablty and expectaton reproducton rate calculated by vector dstance can ncrease the dversty of

6 6 Mathematcal Problems n Engneerng Table 3: The results n Arports Shangha Pudong Shangha Hongqao ngbo Hefe Hangzhou anjng Wenzhou Wux Target value BP QGA-BP DCQGA-BP IDCQGA-BP Table 4: The results n Arports Shangha Pudong Shangha Hongqao ngbo Hefe Hangzhou anjng Wenzhou Wux Target value BP QGA-BP DCQGA-BP IDCQGA-BP quantum chromosomes and can reduce the search space of θ. Then, accordng to lterature [19], ths paper sets the ntal weght s value range of BP neural network as [a, b] (n the numercal computaton, t s [0, 0.38]), whch can accelerate the convergence. 4. The Case 4.1. The Data. The basc data n ths paper comes from eght arports n Yangtze Rver Delta n 2011 and The eght arports are Shangha Pudong arport, Shangha Hongqao arport, ngbo Lshe arport, Hefe Luogang arport, Hangzhou Xaoshan arport, anjng Lukou arport, Wenzhou Yongqang arport, and Wux Shuofang arport. The data of servce radus comes from the defnton. Because Shangha Pudong arport s lsted arport, ts proporton of staff wth college degree or above, total assets, nvestment amount, return on asset, freght throughput, passenger throughput, arcraft movements, and man busness ncome come from annual report. The data of other arports comes from research report and network data. Other data comes from the statstcal yearbook of the cty. 4.2.TheAdvantagesandDsadvantagesoftheAlgorthms. The actvaton functon s 1/(1 + exp( 2x)); other parameters of IDCQGA-BP are shown n Table 2. In order to analyze the advantages and dsadvantages of IDCQGA-BP, ths paper uses BP neural network [12], BP neural network optmzed by quantum genetc algorthm (QGA-BP) [20 22], BP neural network optmzed by double chans quantum genetc algorthm (DCQGA-BP) [16] and BP neural network optmzed by mproved double chans quantum genetc algorthm (IDCQGA-BP) to evaluate the arport compettveness. The deal output s defned as the average value of normalzed man busness ncome and normalzed return on asset. Ths paper runs the four algorthms ten tmes based on the real data n 2011 and The results are the average value of the ten results, as shown n Tables 3 and 4. From Tables 3 and 4,wecangetthefollowngconclusons. (1) In Yangtze Rver Delta, Shangha Pudong arport s an nternatonal hub arport, Shangha Hongqao arport, Hangzhou Xaoshan arport, and anjng Lukou arport are regonal hub arports, and ngbo Lshe arport, Hefe Luogang arport, Wenzhou Yongqang arport, and Wux Shuofang arport are feeder arports. These conclusons are thesameasthosenlterature[23]. (2) All the eght arports compettveness has ncreased from 2011 to 2012, but the ncrease rates are dfferent. ngbo Lshe arport s ncrease rate s the bggest (41%); the man ncrease reason s the ncrease of market condton. Then the medal ones are those of Hefe Luogang arport (16.75%), Hangzhou Xaoshan arport (14.18%), and Wenzhou Yongqang arport (13.76%). Ther man ncrease reason s the mprovement of regonal nfluence. The smaller ones are those of Shangha Pudong arport (5.34%), Shangha Hongqao arport (2.75%), and Wux Shuofang arport (5.45%). Ther man ncrease reason s the mprovement of own strength, especally the ncrease of own nvestment. The errors and runnng tmes are shown n Table 5. From Table 5, tcanbeconcludedthatidcqga-bp s better than the exstng optmzaton algorthms n the aspects of error accuracy and run tme Dynamc Arport Compettveness. hs paper, the arport compettveness n 2011 and 2012 s the ntal value to analyze the dynamc arport compettveness of the eght arports n Yangtze Rver Delta from 2013 to Ths paper supposes that the flght zone levels of the eght arports from 2013 to 2015 are the same as those n The average ncrease rate of the proporton of staff wth college degree or above s 15.2% based on the hstorcal data from 2008 to Theaveragencreaserateof nvestmentamount s13.2%;the number of nonexclusve servce desks remans unchanged. The average ncrease rate of cty resdent transportaton expense s 11.08% based on the cty statstcal yearbook. Theaveragencreaserateof servceradus s2.8%andthe averagencreaserateof thrdndustryoutput s17.7%.

7 Mathematcal Problems n Engneerng 7 Table 5: The errors and runnng tmes of the four algorthms. Algorthms Maxmum error Mnmum error Average error Average runnng tme (s) BP QGA-BP DCQGA-BP IDCQGA-BP Table 6: The overlap condtons of the eght arports servce radus. Overlap or not Pudong Hongqao ngbo Hefe Hangzhou anjng Wenzhou Wux Pudong Hongqao ngbo Hefe Hangzhou anjng Wenzhou Wux ote. 1 stands for overlap, 0 stands for no overlap. The overlap condtons of the eght arports servce radus are shown n Table 6. The nternatonal lnes of the eght arports n 2012 are shown n Table 7. The proporton of the passenger throughput n nternatonal lnes dvded by the total passenger throughput s p t = 20.2%. Based on the ncrease rate analyss, ths paper uses IDCQGA-BP algorthm to calculate the arport compettveness of the eght arports n Yangtze Rver Delta from2013to2015, asshown ntable 8. As shown n Table 8, the eght arports compettveness has ncreased from 2013 to 2015, but the ncrease rates are dfferent. The average ncrease rate of Wux Shuofang arport s the bggest; then the medal ones are those of Hefe Luogang arport and ngbo Lshe arport. The smaller ones are those of Hangzhou Xaoshan arport, anjng Lukou arport, and Wenzhou Yongqang arport. The smallest ones are those of Shangha Pudong arport and Shangha Hongqao arport. The compettveness of feeder arports ncreases sgnally,whoseaveragencreaserate(17.44%)sbggerthan nternatonal hub arport (3.32%) and regonal hub arports (6.94%). The average ncrease rate of regonal hub arports s bgger than nternatonal hub arport. The gaps of the arports n Yangtze Rver Delta are narrowng; the competton and cooperaton are gettng stronger and stronger. The man ncrease reason of Wux arport s the ncrease of own nvestment.morenvestmentcouldleadtosoftwareand hardware condtons upgrade and help attract more arlnes, especally nternatonal flghts. More nvestment could assure more open aero polces. However, blnd extenson should be avoded and more measures should be taken from the aspects of mprovng resource usage effcency such as the effcency of captal, flghts, and human resources. 5. Concluson hs paper, the arport compettveness when arports are facng competton and cooperaton s studed. The emprcal study s based on the data of eght arports n Yangtze Rver Delta n 2011 and 2012, and the eght arports dynamc compettveness under competton and cooperaton from 2013 to 2015 s calculated. The average ncrease rate of feeder arports s bgger than nternatonal hub arport and regonal hub arports. The average ncrease rate of regonal hub arports s bgger than nternatonal hub arport. The gaps of the arports n Yangtze Rver Delta are narrowng; the competton and cooperaton are gettng stronger and stronger. On the whole, the contrbuton of ths paper to the lterature s emboded n two aspects. Frstly, a new algorthm IDCQGA-BP algorthm s proposed. From the results, t can be concluded that IDCQGA-BP s better than the exstng optmzaton algorthms n the aspects of error accuracy and run tme. IDCQGA-BP has satsfactory mtatve effect and hgh accuracy; ts applcablty has been verfed. Secondly, The paper smulates the nfluence of competton and cooperaton on arports market share and calculates the dynamc arport compettveness when arports are facng competton and cooperaton. It flls the gap of the exstng lterature n whch only statc arport compettveness has been evaluated and lays good foundaton for evaluatng dynamc arport compettveness reasonably. However, t should be noted that the parameter selecton process has certan randomness. Ths paper runs the algorthm ten tmes to mnmze the effect caused by ths randomness, but ths wll ncrease the workload. Future research could focus on avodng the randomness to reduce the workload.

8 8 Mathematcal Problems n Engneerng Table 7: The nternatonal lnes of the eght arports n Arports Shangha Pudong Shangha Hongqao ngbo Hefe Hangzhou anjng Wenzhou Wux umber Table 8: The dynamc arport compettveness of the eght arports from 2013 to Arports Shangha Pudong Shangha Hongqao ngbo Hefe Hangzhou anjng Wenzhou Wux Average ncrease rate (%) Acknowledgments Ths research s funded by atonal ature Scence Foundaton of Chna (nos , , and ) and the Fundamental Research Funds for the Central Unverstes (no ) and supported by Program for Laonng Innovatve Research Team n Unversty. References [1] R. Leshout and H. Matsumoto, ew nternatonal servces and the compettveness of Tokyo Internatonal Arport, Transport Geography, vol. 22, pp , [2] Y. Park, An analyss for the compettve strength of Asan major arports, Ar Transport Management, vol. 9, no. 6, pp , [3] H. Lee and H. M. Yang, Strateges for a global logstcs and economc hub: Incheon Internatonal Arport, Ar Transport Management,vol.9,no.2,pp ,2003. [4] Y. Park, Applcaton of a fuzzy lngustc approach to analyse Asan arports compettveness, Transportaton Plannng and Technology,vol.20,no.4,pp ,1997. [5] C.-H. Yeh, Y.-L. Kuo, and Y.-H. Chang, Fuzzy multattrbute evaluaton of arport performance, n Proceedngs of the IEEE Internatonal Conference on Fuzzy Systems (FUZZ 11),pp , June [6] J.L.PengandC.X.Zhan, Acasestudyonevaluatonofarport logstcs compettveness based on AHP, Advanced Materals Research, vol. 159, pp , [7] X. Y. Wu and K. Y. Wu, Study on the competency of Shangha Pudong Arport n Asa-Pacfc Ocean wth AHP, Logstcs Technology,vol.9,pp ,2005. [8] C.Cheng,K.S.L,andR.Lu, Researchontheelementsand enhancement measures of arport compettveness: the case of Guangzhou Bayun Internatonal Arport, Industral&Scence Trbune,vol.11,no.2,pp.24 25,2012. [9] Q.Cu,H.B.Kuang,C.Y.Wu,andY.L, Dynamcformaton mechansm of arport compettveness: the case of Chna, Transportaton Research A,vol.47,no.1,pp.10 18,2013. [10] S. Garell, Top Class Compettors, Orent Press, Bejng, Chna, [11] P. F., Chnese Cty Compettveness Blue Book 2010: Chna Cty Compettveness Report, Socal Scences Documentaton Publshng House, Bejng, Chna, [12] W. Wu, eural etwork Computaton,HgherEducatonPress, Bejng, Chna, [13] FECIT Technologcal Product Research Center, eural etwork Theory and MATLAB 7 Applcaton, Publshng House of Electroncs Industry, Bejng, Chna, [14] R. P. Feynman, Smulatng physcs wth computers, Internatonal Theoretcal Physcs,vol.21,no.6-7,pp , [15] K.-H. Han and J.-H. Km, Quantum-nspred evolutonary algorthm for a class of combnatoral optmzaton, IEEE Transactons on Evolutonary Computaton, vol.6,no.6,pp , [16] S. Y. L and P. C. L, Quantum Computaton and Quantum Optmzaton Algorthms, Harbn Insttute of Technology Press, Harbn, Chna, [17] M. X. Sun and X. P. Chen, A mmune algorthm based on the vector dstance appled to functon optmzaton, Suzhou Unversty Engneerng Scence Edton,vol.30,no.3,pp , [18] B.Wang,Z.Zhang,F.L,Y.Sun,andH.Dng, Comprehensve evaluaton of regulated defct rrgaton usng projecton pursut model based on mproved double chans quantum genetc algorthm, Transactons of the Chnese Socety of Agrcultural Engneerng,vol.28,no.2,pp.84 89,2012. [19] S. Qao and Z. H. Dong, A method of choosng BP network s ntal weghts, ortheast ormal Unversty,vol.36, no.3,pp.25 30,2004. [20] Q. Zuo, S. S. Ye, R. C. Guo, and H. Sh, Usng quantum genetc algorthm to mprove BP learnng algorthm, Computer System &Applcatons,vol.2009,no.5,pp.53 55,2009. [21] Y. G. Ca, M. J. Zhang, H. Ca, and Y. Zhang, Hybrd chaotc quantum evolutonary algorthm, Systems Engneerng Theory & Practce, vol. 32, no. 10, pp , [22] Y.-H. L and Y.-P. Wang, An effectve hybrd quantum genetc algorthm, Systems Engneerng Theory & Practce,vol.26,no. 11, pp , [23] Y. B. Yang and S. Zhong, A classfcaton of Chnese cvl arports, Arport, vol. 11, pp , 2004.

9 Advances n Operatons Research Advances n Decson Scences Appled Mathematcs Algebra Probablty and Statstcs The Scentfc World Journal Internatonal Dfferental Equatons Submt your manuscrpts at Internatonal Advances n Combnatorcs Mathematcal Physcs Complex Analyss Internatonal Mathematcs and Mathematcal Scences Mathematcal Problems n Engneerng Mathematcs Dscrete Mathematcs Dscrete Dynamcs n ature and Socety Functon Spaces Abstract and Appled Analyss Internatonal Stochastc Analyss Optmzaton