A particle swarm optimization algorithm for optimal car-call allocation in elevator group control systems

A pa icle swa m op imiza ion algo i hm o op imal ca -call alloca ion in ele a o g oup con ol sys ems Be na Bola Yildiz Technical Uni e si y, Facul y o Mechanical Enginee ing, Mechanical Enginee ing Depa men , Yildiz, TR-34349, Is anbul, Tu key. Email: [email protected]. Oğuz Al un Yildiz Technical Uni e si y, Facul y o Compu e Enginee ing, Compu e Enginee ing Depa men , Yildiz, TR-34349, Is anbul, Tu key. Email: ogu[email p o ec ed]. Pablo Co és Uni e si y o Se ille, Escuela Técnica Supe io de Ingenie ía, Ingenie ía O ganización Camino de los Descub imien os s/n, Se illa 41092, Spain. Email: [email protected] Abs ac .- High- ise buildings equi e he ins alla ion o complex ele a o g oup con ol sys ems (EGCS). In e ical anspo a ion, when a passenge makes a hall call by p essing a landing call bu on ins alled a he loo and loca ed nea he ca s o he ele a o g oup, he EGCS mus alloca e one o he ca s o he g oup o he hall call. We de elop a Pa icle Swa m Op imiza ion (PSO) algo i hm o deal wi h his ca -call alloca ion p oblem. The PSO algo i hm is compa ed o o he so compu ing echniques such as gene ic algo i hm and abu sea ch app oaches ha ha e been p o ed as e icien algo i hms o his p oblem. The p oposed PSO algo i hm was es ed in high- ise buildings om 10 o 24 loo s, and se e al ca con igu a ions om 2 o 6 ca s. Resul s om ials show ha he p oposed PSO algo i hm esul s in be e a e age jou ney imes and compu a ional imes compa ed o gene ic and abu sea ch app oaches. Keywo ds: Ele a o ; li ; pa icle swa m op imiza ion; ele a o g oup con ol sys em; e ical anspo a ion 1. In oduc ion High- ise buildings equi e he ins alla ion o la ge ele a o g oups. The e o e, he e ical anspo a ion indus y g ows oge he wi h he mo e and mo e inc ease o such high- ise buildings. Such si ua ions equi e he managemen o mul iple ele a o s in a coo dina ed way in o de o e icien ly anspo passenge s h oughou he building. This managemen is done by he Ele a o G oup Con ol Sys em (EGCS). The EGCS pe o mance depends on he a ic pa e n in he building. I ep esen s he mobili y o a building popula ion in i s necessi ies o e ical anspo a ion. Each building add esses a speci ic shape o i s own a ic pa e n. Typically, in a p o essional building he a ic pa e n will p esen a la ge han a e age numbe o up landing calls a he s a o he day. These a e due o he building’s wo ke s a i ing o s a wo k. This phase is called uppeak a ic. On he con a y, la e in day he e is he opposi e phenomenon, and a la ge han a e age numbe o down landing calls akes place. I co esponds o he building’s popula ion wan ing o go home a e a wo king day. This a ic pa e n is called downpeak. In he middle o he day he e a e wo join phenomena, because he appea ance o up and down peaks. I depic s a si ua ion o people wan ing o lea e he building o lunch and people coming back a e lunch. This pe iod is called lunchpeak a ic. Finally he es o he day does no show any special endency om any speci ic loo o om any speci ic s eam. Gene ally, less a ic is egis e ed oo. I is called in e loo a ic. I has been p o en (Ba ney e al. 1985; o Cibse Guide D (2000) amongs o he s) ha uppeak a ic is he mos s essed a ic ha is p oduced in a high- ise building. Thus, acco ding o ha we unde ake he analysis o ou algo i hm unde such condi ions. In gene al e ms, he main decision he con olle has o ake is o de e mine which ca om he EGCS (one speci ic li om he ele a o g oup) mus be assigned o a call. Tha is, once a passenge wan s o a el om a loo o ano he di e en loo in a building, and he passenge makes a hall call o an ele a o by p essing a landing call bu on ins alled a he loo and loca ed nea he ca s o he ele a o g oup, he EGCS mus iden i y he ele a o in he g oup ha is mos sui able o se e he passenge . Thus, he p oblem o be sol ed is o selec an ele a o o each hall call ha is issued. This p oblem is called as he ca -call alloca ion p oblem. The op imiza ion o such p oblem is mainly based on he minimiza ion o he a e age jou ney ime (AJT), which is he ime in seconds ha a passenge spends a elling o a des ina ion loo measu ed om he ins an o call egis a ion o he ins an passenge s eps on o he des ina ion loo . AJT ime consis s o he a e age a el ime (ATT), which consis s o he ca a el ime om he o igin loo o he des ina ion plus he a e age wai ing ime (AWT), which consis s o he ime in seconds ha a passenge wai s o se ice measu ed om he ins an a passenge egis e s a call o he ins an he passenge en e s an ele a o ca s. See Ba ney e al. (1985) and he Cibse Guide D on T anspo a ion sys ems in buildings (2000), which p obably cons i u e some o he mos ecognized handbooks in e ical anspo a ion. These pa ame e s a e also discussed in he simula ion sui e ha is p oposed in Co és e al. (2006). Recen ly, some au ho s (Tyni e al. 2006 o Hasan e al. 2012) a e conside ing he ene gy consump ion o he sys em as objec i e unc ion o low a ic si ua ions and basing i s analysis on he use o he ene gy gene a ion by he coun e weigh . I is called he ene gy p oblem o he e ical anspo a ion sys em. In his pape , we ocused on he ca -call alloca ion p oblem is NP-Ha d independen ly o he c i e ion. Thus, mos o he app oaches a e ocused on so compu ing echniques. In ac , e ical anspo a ion has become a majo ield o applica ion o so compu ing app oaches such as uzzy logic, neu al ne wo ks, gene ic algo i hms, e c. (see sec ion 2). All o hem a e echniques capable o p o iding be e solu ions han adi ional con olle s implemen ing dispa ch expe ules ha make use o simple IF-ELSE logical command se s. The es o he pape ollows wi h he p esen a ion is o ganized as ollows: sec ion 2 desc ibes ela ed wo k unde aken o sol e he ca -call alloca ion p oblem in ele a o g oup con ol sys ems; he PSO algo i hm ha we implemen ed is desc ibed in sec ion 3; he compu e simula ions a ending o he a e age jou ney ime and compu a ional ime o PSO algo i hm and i s compa ison wi h gene ic algo i hm and abu sea ch app oaches a e deal in sec ion 3; and inally main conclusions a e discussed in sec ion 4. 2. Rela ed wo k As i has been p e iously add essed in sec ion 1, he implemen a ion o e icien con ol sys ems in ele a o g oups is absolu ely equi ed o all buildings. Such ype o sys ems a e called Ele a o G oup Con ol Sys ems (EGCS), and al hough his is a young ield o esea ch (acco dingly wi h he ecen e olu ion o he elec onic) i can be ound ele an e e ences in he scien i ic li e a u e. Mos o mode n EGCSs implemen complex algo i hms based on di e en so compu ing echniques. Gene ic algo i hms appea ed as one o he i s a emp s o ackle wi h such p oblem, and since hen ha e been widely used p o iding good and aluable esul s. Examples o ha a e Co és e al. (2003) and Co és e al. (2004), whe e he au ho s implemen ed a bina y gene ic encoding o easible solu ions ha has been widely ollowed by se e al au ho s since hen. The algo i hm was compa ed o adi ional indus y collec i e con olle s ( ha a e no based on so compu ing app oaches) p o iding signi ican imp o emen s. The compa ison was unde aken using simula ion ARENA so wa e. The main p oblem o hese app oaches was he equi ed compu a ion ime equi ed o he mic ochips o he con olle s. A e ha , gene ic app oaches con inued being applied. I was he case o Tyni e al. (2006) ha de eloped a bi- objec i e gene ic algo i hm ha op imized he wai ing imes and he ene gy consump ion o he KONE Co po a ion. La e , Hi asawa e al. (2008) applied ano he gene ic app oach o a speci ic ype o ele a o s ha a e called double-deck whe e wo cages a e connec ed in a sha ha e been de eloped o he ising demand o mo e e icien anspo o passenge s in high- ise buildings. He e, he au ho s de elop a g aph-based e olu iona y me hod named gene ic ne wo k p og amming ha in oduce a ious node unc ions ha can be easily execu ed by an e icien ule-based g oup supe iso y con ol ha is op imized in an e olu iona y way. Mo e ecen ly, Bola e al. (2010) de eloped a gene ic algo i hm based on he p e ious wo k o Co és e al. (2004) p o iding a no el way o compu e he a e age wai ing ime o passenge s ha allowed a compu a ionally e ec i e e alua ion, and o e coming ha limi a ion. Tabu sea ch has a ac ed less a en ion han gene ic algo i hms, al hough ecen ly wo algo i hms based on de e minis ic and p obabilis ic app oaches ha e been p esen ed o deal wi h he p oblem. Bola e al., (2011) ha e p o ided a de e minis ic and p obabilis ic app oach o he abu sea ch algo i hm ha allows ou pe o m he esul s p o ided by he equi alen gene ic implemen a ion. Li e al. (2007) has ied immune sys ems based on a wo-le el con ol s uc u e. One s uc u e is he locally op imal assignmen o a hall call pe o med by a con en ional collec i e algo i hm; he o he is he globally op imal assignmen o all hall calls, which is execu ed pe iodically by a i icial immune algo i hm. This ep esen s one o he e y ew app oaches based on immune sys ems o deal wi h e ical anspo a ion p oblems. Con ol me hodologies ha e been applied o EGCS oo. I is he case o neu al ne wo ks ha we e e y en husias ically ied in he o igins o he discipline (as i was he case o Im ak e al., 2001). Mo e ecen ly, he same au ho (Im ak, 2008) has p esen ed an e olu ion o his i s app oach and has compa ed i wi h indus y con en ional app oaches. The pape shows how he EGCS can p edic he nex s opping loo s o s op by conside ing wha has been lea n by p ocessing he changes in passenge se ice demand pa e n. Echa a ia e al. (2009) ha e de eloped a eed- o wa d neu al ne wo k based con ol algo i hm has been de eloped ha can app oxima e ele a o call pa e ns by lea ning o associa e ime o day wi h speci ic call loca ions. A b ie compa ison o he me hod allows an icipa ing sui abili y when compa ing o uzzy app oaches, al hough u u e esea ch is equi ed o gua an ee such claim. Mo e ecen ly, Du sun (2010) has implemen ed a neu al ne wo k o con ol he EGCS o cases o ex e nal bu ons. I co esponds o he case o e ical anspo a ion zoning app oaches. The app oach p o ided ad an ages o con en ional s a ic zoning app oaches. Al hough uzzy logic was also conside ed since a long ime ago (see Kim e al., 1998 o one o he i s a emp s o deal wi h uzzy con olle s), nowadays, he ex ensi e use o uzzy logic is being implemen ed in an en husias ic way. I is he case o Jamaludin e al. (2010) ha ha e p esen ed a uzzy con olle ha ins ead o depending hea ily on he p edic ed passenge a ic pa e n o adap a ion, he uzzy logic g oup con olle adjus s i sel o sui he sys em’s en i onmen h ough a sel - uning scheme. Resul s a e simula ed and compa ed o con en ional app oaches showing a signi ican imp o emen . La e , Co es e al. (2011) ha e de eloped a uzzy con olle o o ecas he a ic pa e ns in he e ical anspo a ion sys em. The con olle allows o iden i y whe he sys ems is unde an uppeak, downpeak, lunchpeak o in e loo pa e n. Recen ly, Chen e al. (2012) ha e p esen ed a uzzy logic app oach o con ol he EGCS sel - uned by a gene ic algo i hm o maximize se ice quali y and managing a wide se o a iables such as he numbe o ele a o s, a ic low, di ec ion, conges ion, p io i y o loo , and p e e ence o passenge s, amongs o he s. He e we p esen a pa icle swa m op imiza ion (PSO) algo i hm based on a hall call alloca ion s a egy o de ine he solu ion encoding and compu a ionally e ec i e ca -call alloca ion quali y es ima ion. I cons i u es a no el applica ion o PSO o a new ele an indus y sec o . App oaches based on PSO a e sca ce in he li e a u e. Li (2010) PSO algo i hm was an excep ion. Li p esen ed a PSO algo i hm ha is used o op imize ypical zoning ele a o p oblems whe e he loo sec o ha is going o be se ed by each ele a o ca has o be s a ed. The app oach shows good esul s when compa ing o o he echniques. Zoning (o sec o ing) app oach o e ical anspo a ion is a echnique used o se e skysc ape s du ing he uppeak a ic. I di ides he building in o di e en sec o s and assign one (o a g oup o ) ca o each sec o . When he ca lea es he passenge s in he loo s assigned o i s zone, he ca go down o collec mo e passenge s a elling o he loo s o he zone. This echnique is only applied o uppeak condi ions because i wo sens he EGCS pe o mance du ing o he pa e ns (downpeak, lunchpeak o in e loo ). I also equi es he ins alla ion o ex e nal bu on box wi h all he loo des ina ion numbe in he hall o he ca . Thus, i ela es o o he e ical anspo a ion philosophy o managemen . This is called he zoning p oblem a di e ence om he dispa ching p oblem (o ca -call alloca ion p oblem). When using zoning app oaches, one o he main pa ame e s o be op imised is he ound ip ime (RTT), which measu es he ime equi ed o ake a passenge om he g ound loo , go up o he highes loo and come back o he g ound loo . Hence, RTT o mulas can only be applied o up a ic whe e passenge s en e o he lobby and a e des ined o uppe loo s. The e o e, due o hese easons, we canno compa e ou algo i hm ha is con igu ed o a adi ional bu on box wi h only up and down bu ons and ha do no conside zoning app oach o dispa ching ca s, bu a global EGCS whe e all he ca s se e all he loo s o he building. Howe e , he good esul s p o ided by PSO o he zoning p oblem, lead us o y wi h he app oach o he ca -call alloca ion p oblem p o iding ou s anding esul s as i is shown in he esul sec ion. To ha e benchma ks capable o assessing ou p oposal, we compa e i agains iden ical objec i e unc ion implemen a ions and he same building con igu a ion o gene ic algo i hms and abu sea ch app oaches. In his line, esul s a e p o ided o high- ise buildings om 10 o 24 loo s, and se e al ca con igu a ions om 2 o 6 ca s. 3. The pa icle swa m op imiza ion algo i hm o EGCS In eal buildings, passenge s a i e andomly o di e en loo s, e en a he same ime, wishing o be anspo ed om a loo o o he one. In addi ion, buildings show speci ic mo emen o passenge s ha can de e mine he low pa e n in he building. Fou main pa e ns a e adi ionally ca alogued: (i) uppeak a ic when a la ge han a e age numbe o up landing calls a e p oduced ( ypically because he building’s wo ke s a i ing o s a wo k); downpeak a ic when a la ge han a e age numbe o down landing calls akes place (because building’s popula ion wishes o go back home a e he wo king day); (iii) lunchpeak a ic ha akes place in he middle o he day, and i is due o he appea ance o up and down peaks; and inally (i ) in e loo a ic co esponding o he es o he day. The la e phenomenon can be cha ac e ised o a low demand (usually a ound 4% o he popula ion) in bo h di ec ions. The ele a o g oup con ol sys ems de e mine which ca o he g oup should se e a hall call. I he hall call is alloca ed o he mos app op ia e ca , he passenge s’ a el and wai ing imes a e educed. We de elop he e a PSO algo i hm ha ou pe o ms o he so compu ing implemen a ions such as gene ic algo i hm o abu sea ch. Nex we de elop he solu ion encoding o he cha ac e iza ion o p oposed solu ions ha is desc ibed in nex subsec ion 3.1; he way o assess he ca -call alloca ion, i.e., he e alua ion o he candida e solu ions (wha is called as i ness); and he de ailed desc ip ion o he algo i hm and i s lowcha . 3.1. Ca -call alloca ion solu ion encoding Hall calls a e encoded using a 2×(Numbe o loo s – 1) elemen s a ay. The i s hal o he a ay co esponds o upwa ds landing calls, and he second hal co esponds o downwa ds landing calls. A speci ic ca om he ele a o g oup mus be alloca ed o each eques ed hall call, and a solu ion o he ca -call alloca ion p oblem consis s o he alloca ion o a speci ic ca o he ele a o g oup o all he hall calls being eques ed in he building. The e o e, he dimension o he solu ion becomes equal o numbe o eques s in he hall call a ay (‘ones’ in Figu e 1.a), as no ele a o needs o be assigned o loo s wi hou eques s. So, he dimension o he solu ion encoding can change acco ding o he di e en hall call eques con igu a ions. Figu e 1 (a) and (b) p o ides an example o ca -call alloca ion solu ion encoding o a building wi h 10 loo s and 3 ele a o s. Figu e 1 (a) ela es o he hall call eques ed a he loo s. I is ep esen ed by an a ay o 18 elemen s, 9 o upwa ds landing calls, and 9 o downwa ds landing calls. A numbe equal o ‘1’ means ha he e is a call, and a ‘0’ means ha he e a e no calls a ha loo . Figu e 1 (b) ela es o he solu ion encoding o he ca -call alloca ion p oblem. In he example, Figu e 1 (a) shows ha he equi ed solu ion encoding will ha e a dimension equal o 13; such dimension co esponds o he eques ed hall calls. A solu ion consis s o he alloca ion o a ca o he ele a o g oup (composed o h ee ca s) o a hall call. The solu ion encoding in he example shows he numbe o he ca ha is assigned. Hence, each elemen can ge a alue be ween 1 and 3 since we ha e 3 ele a o s. Upwa ds Landing Calls Downwa ds Landing Calls F1 F2 F3 F4 F5 F6 F7 F8 F9 F2 F3 F4 F5 F6 F7 F8 F9 F10 1 0 1 1 0 1 1 1 0 1 1 1 1 0 1 1 1 0 (a) F1 F3 F4 F6 F7 F8 F2 F3 F4 F5 F7 F8 F9 3 1 2 3 3 1 2 1 2 3 2 1 3 (b) Figu e 1. (a) An example o hall call encoding o a building o 10 loo s. (b) Co esponding solu ion encoding o an ele a o g oup wi h 3 ca s. 3.2. Ca -call alloca ion i ness e alua ion To e alua e he quali y o a ca -call alloca ion by he EGCS is, we e alua e he i ness o such alloca ion. As any possible alloca ion mus be e alua ed, compu a ionally e ec i e is o eno mous ele ance. The e o e, he me hod o e alua e he i ness unc ion is a e y impo an issue. In addi ion, he i ness has o p o ide an adequa e alue o he pe o mance o he ca -call alloca ion. One o he mos ime-implemen ed dispa che s in he ele a o indus y is he THV one (see Co és e al., 2003). THV algo i hm was implemen ed a UMIST (Uni e si y o Manches e Ins i u e o Science and Technology), and assigns he hall call o he nea es li in he adequa e ip di ec ion (some imes appea s e e ed as nea es call algo i hm). I is easy o be implemen ed bu does no p o ide high quali y es ima ions o he p oposed alloca ion. Ano he common implemen a ion is due o he es ima ed ime o a i al (e en a he same ime, wan ing) algo i hm, which unde akes an es ima ion o he equi ed ime since he landing call is issued un il he ca a i es (Ba ney e al., 1985). ETA includes di e en le els o p io i y: (i) long wai ing calls; (ii) high ac i i y loo s; (iii) p io i y le els; and (i ) emaining calls. Those ca s a ending calls wi h p io i y le el one o h ee do no s op a landing calls and only se e ca calls equi ing a special AJT calcula ion. ETA algo i hm p o ides a sui able beha io du ing uppeak a ic, a medium le el se ice o in e loo , and a bad le el o se ice du ing lunchpeak and specially downpeak. These app oaches we e es ed in Co es e al. 2004, Bola e al. 2010 and i was app ecia ed ha he Bola e al. (2011) i ness e alua ion p oposal ou pe o med he o he p e iously desc ibed app oaches bo h in quali y o solu ions and compu a ional speed. So hen, we op ed o ollow he me hodology desc ibed in Bola e al. (2011) as an easy- o-implemen and as - o-compu e echnique, p o iding he be e index o pe o mance o he sys em in a sui able ime o esponse. I is desc ibed nex . Gi en he ollowing pa ame e s: - Ψ 1 : g ound loo le el - Ψ 2 : highes down hall call le el - Ψ 3 : numbe o down hall calls be ween Ψ 1 and Ψ 2 . - Ψ 4 : highes up hall call le el - Ψ 5 : numbe o up hall calls be ween Ψ 1 and Ψ 4 . - Ψ 6 : lowes down hall call le el - : doo opening and closing ime - p : passenge ans e ime - Hc : Highes ca ip ime - Lc : lowes ca ip ime The solu ion i ness, , is calcula ed depending on he ype o passenge s’ mo emen s. So, a i ness alue is i s ly calcula ed o each ca , i, in he g oup by conside ing ou di e en cases ha a e shown in igu e 2. No e ha each o mula ela es in which loo he passenge is aken and in o which loo he passenge is ans e ed. Hence, as a gene al de ini ion o Ψ, i shows he loo s whe e passenge s a e ge ing on and o . CASE 2 Only up hall calls uppeak a ic pa e n CASE 3 Only down hall calls downpeak a ic pa e n CASE 4 Down and up hall calls lunchpeak and in e loo a ic pa e ns ( ) ( ) 2 1 3 1 Ψ − Ψ  =   + Ψ − Ψ     i p ( ) ( ) 4 1 5 1 Ψ − Ψ  =   + Ψ − Ψ     i p ( ) ( ) ( ) ( ) ( ) 4 1 2 4 2 6 3 5 1   Ψ − Ψ + Ψ − Ψ +   =   Ψ − Ψ + Ψ + Ψ − Ψ   i p 0 = i CASE 1 No hall calls Figu e 1. Fi ness es ima ion depending on he a ic pa e n Finally he g oup i ness is calcula ed, see equa ion (1), and he inal i ness is e alua ed o he p oposed alloca ion, see equa ion (2). Tha is, he o al i ness o he sys em is calcula ed as a wo-pa unc ion whe e he i s pa collec s he conce n ela ed o passenge s wai ing in he halls, and he second pa conce n ela ed o an unbalanced pe o mance in he ca s o he g oup. Tha is, he second pa ies o le el he use o each ca . Le ’s conside he ollowing example: ca no.1 is wo king du ing 100 seconds and ca no.2 du ing 11 seconds, so bo h ca s would be wo king du ing 111 seconds bu in a much unle elled manne . On he o he hand i ca no.1 wo ks du ing 57 seconds and ca no.2 du ing 54 seconds, he wo ca s wo k du ing 111 seconds bu in a much mo e le elled manne . This ac ion will ha e epe cussion on he li e cycle o he ca s. Al hough k 1 and k 2 a e design pa ame e s, and a ia ions and a disc e ional c i e ion can be admi ed, we selec hem equal o 1.5 and 2 acco dingly wi h Bola e al. (2011). 1 being he numbe o ca s in he g oup n i i g oup , n n = = ∑ (1) ( ) 1 2g oup k · k · Hc Lc = + − (2) 3.3. Pa icle swa m op imiza ion algo i hm Pa icle swa m op imiza ion, p esen ed in Kennedy e al. (1995), is a popula ion based s ochas ic op imiza ion echnique inspi ed by social beha iou o bi d locking o ish schooling. PSO is a popula ion based echnique. The sys em is ini ialized wi h a popula ion o pa icles ha co espond o ini ial solu ions o he p oblem. These pa icles mo e in he sea ch space sea ching o op imal i ness alue. Following Coelho (2010), PSO algo i hm de ines pa icle as a po en ial solu ion ep esen ed by an s-dimensional ec o , whe e s is he numbe o op imiza ion a iables. The swa m concep ela es o an appa en ly diso ganized popula ion o mo ing pa icles ha ends o clus e oge he while each pa icle seems o be mo ing in a andom di ec ion. The pa icle bes posi ion is calcula ed o a pa icle mo ing h ough he sea ch space, i compa es he i ness alue a he cu en posi ion o he bes i ness alue i has e e a ained a any ime up o cu en ime. Then, he global bes ela es o he bes posi ion among all indi idual bes posi ion; and he eloci y o he pa icle ligh ep esen s he eloci y o he pa icle in he physical analogy, aking in o ha all he pa icles eloci ies and posi ions a e upda ed a e each i e a ion. We implemen ed a PSO algo i hm ha is desc ibed by he lowcha in Figu e 3. The s eps o he lowcha a e numbe ed o easy e e ing. S eps 1 o 4 ini ialize a iables ha change in he main loop in a sui able way be o e en e ing in o he main loop (s ep 5). In s ep 1, each pa icle is “ h own” o some andom place in he sea ch space: each pa icle is gi en a andom posi ion. Then, each pa icle ge s a andom eloci y (s ep 2). Since a pa icle has a posi ion and a gi en eloci y, i s nex posi ion can be calcula ed in he i s i e a ion o he main loop ollowing he ules and calcula ions de ailed in s eps 8 and 9 la e . In s ep 3, he i ness alue o each pa icle’s ini ial posi ion is calcula ed. The bes o hese ini ial posi ions is assigned o he a iable “global bes ” (s ep 4). S eps 5 o 14 cons i u e he main loop o he algo i hm. In s ep 5, i is decided whe he ano he i e a ion o he loop is unde aken o no by checking he numbe o i e a ions. Once he maximum numbe o i e a ions is eached, he p ocedu e ge s ou o he loop and p oceeds o s ep 15. S ep 6 makes su e ha all he pa icles a e p ocessed. In s ep 7, we ge he nex unp ocessed pa icle, and p oceed wi h i . Le us call his pa icle he “cu en ” pa icle. In s ep 8, a new eloci y is se o he cu en pa icle using nex equa ion (3): g wl w w jj ∆ + ∆ + = +231211 , (3) whe e 1+j is he new eloci y ec o , j is he cu en eloci y ec o , 1 w , 2 w , and 3 w a e cons an scala s (weigh s), 1 and 2 a e ec o s whose elemen s a e andom alues be ween 0and 1 , l ∆ is he di e ence ec o be ween he bes and cu en posi ion o he pa icle, and g ∆ is he di e ence ec o be ween he global bes posi ion and he pa icles cu en posi ion. This equa ion explains ha he new eloci y o he pa icle is calcula ed using h ee alues: he cu en eloci y, he dis ance o he pa icle o i s bes posi ion, and i s dis ance o he global bes posi ion. Weigh s 1 w , 2 w , and 3 w allow us o se ela i e impo ance o he h ee e ms in de e mining new eloci y. A e se e al es s, we concluded ha no pa o equa ion (3) should be p io i ized o ano he in o de o ge he bes pe o mance o he algo i hm. So hen, alues we e se o 0.34, 0.33, and 0.33 o make hem oughly equal, and o make hem sum up o 1. In s ep 9, he cu en posi ion oge he wi h he new eloci y de e mines he new posi ion (each i e a ion was assumed as a uni ime). In some occasions, he new calcula ed posi ion could no be alid, e.g. hey a e ou o he bounds o he sea ch space. This ac is checked in s ep 10, and i he posi ion is no alid a andom alid posi ion is assigned o he pa icle. And he new eloci y alue is e-upda ed acco ding o his new posi ion (s ep 11). S eps 12 o 14 a e o bookkeeping and p epa a ion o he nex i e a ion. S ep 12 calcula es he i ness alue o he new posi ion. I necessa y, he pa icle’s bes posi ion and global bes posi ion a e also upda ed (s eps 13 and 14). When he maximum numbe o i e a ions is eached, he p ocedu e ge s ou o he main loop and mo es o nex s ep 15. The global bes e ains he esul o he op imiza ion. A e es ing se e al alues, we ealized ha a maximum numbe o i e a ions equal o 30 we e enough o gua an ee con e gence wi hou penalising compu a ional imes. In a simila line, a numbe o 30 pa icles gua an eed a sui able mapping o he al e na i e solu ions. Inc easing he i e a ions and he numbe o pa icles did no epo signi ican imp o emen s meanwhile he equi ed ime o un he algo i hm inc eased in alues ou o eal applicabili y in he ele a o indus y. 5. Conclusion We ha e p esen ed a no el applica ion o PSO algo i hm o op imize he ca -call alloca ion s a egy o he con olle in Ele a o G oup Con ol Sys ems. PSO algo i hms had been ied in e ical anspo a ion p oblems o deal wi h sec o ing p oblems, which is a echnique used o se e skysc ape s du ing he uppeak a ic di iding he building in o di e en sec o s and assigning one (o a g oup o ) ca o each sec o . Ou PSO implemen a ion o sol e he ca -call alloca ion p oblem, also known as he dispa ching p oblem, cons i u es a no el y in he ele a o scien i ic li e a u e. Ou implemen a ion is based on a hall call alloca ion s a egy o de ine he solu ion encoding and includes compu a ionally e ec i e ca -call alloca ion quali y es ima ion. Resul s we e p o ided o high- ise buildings om 10 o 24 loo s, and se e al ca con igu a ions om 2 o 6 ca s. The algo i hm was success ully applied o all he case s udies ou pe o ming o he so compu ing echniques such as gene ic algo i hms and abu sea ch. Resul s we e be e a ending o he a e age jou ney ime ( ha includes he wai ing plus a el imes) as well as a ending o he algo i hm compu a ional imes. E en mo e he obus ness o he me hod (measu ed as he s anda d de ia ion) was also p o en. Hence, i was shown ha PSO go be e esul s in a as e , cheape way compa ed wi h he o he me hods. Ano he eason ha made PSO mo e a ac i e wi h espec o o he echniques is i s capabili y o be adjus ed wi h e y ew pa ame e s, which adds addi ional obus ness. Howe e , esul s ob ained wi h a CPU can be limi ed wi h espec he eal indus y ope a ion. In p ac ice, eal implemen a ion should be ins alled in speci ic mic ochips, which would equi e ligh e implemen a ions. Bounding his ac , he eal implemen a ion o he PSO algo i hm in he indus y appea s o be possible. Fo example, in eal cases an al e na i e can be calcula ing he i ness no e e y ime bu in a selec i e manne , o s opping he algo i hm a e a lowe numbe o i e a ions. O cou se all hese decisions a e e y dependen on he compu a ion speed o he elec onic mic ochips ins alled by he company in he con olle , and could a ec he quali y o he implemen ed algo i hm. Cu en ly, ou u he esea ch ocuses on global con olle s capable o iden i ying a ic pa e n aking pa in he building and launching he co esponding specialised algo i hm. This global con olle in ends o inco po a e he conside a ion o ene gy op imiza ion o low demand pa e ns such as in e loo a ic pa e n ha can p oduce a signi ican educ ion o ene gy consump ion wi hou wo sening e y much he quali y o se ice indexes. Acknowledgemen s The Spanish au ho acknowledges he inancial suppo gi en by he Counselling o Inno a ion, Science and Business o Andalusia, h ough i s Excellence P ojec s P og amme (p ojec e . P07-TEP-02832), and he Spanish Resea ch Agency dependen on he Depa men o Science and Inno a ion, h ough i s DPI P og amme (p ojec e . DPI2010- 15352). Re e ences [1] G. Ba ney, S. Dos San os (1985) Ele a o a ic analysis design and con ol , Pe e Pe eg inus L d, Lond es. [2] B. Bola . P. Co és, E. Yalcin, M. Alis e isci (2010) Op imal ca dispa ching o ele a o g oups using gene ic algo i hms, In e na ional Jou nal o In elligen Au oma ion and So Compu ing , 16(1), pp. 89-99. [3] B. Bola , P. Co és (2011) Gene ic and abu sea ch app oaches o op imizing he hall call- ca alloca ion p oblem in ele a o g oup sys em, Applied So Compu ing , 11(2), pp.1792- 1800. [4] Cha e ed Ins i u ion o Building Se ices Enginee s. CIBSE Guide D (2010) T anspo a ion sys ems in buildings , London. [5] T-C. Chen, Y-Y. Hsu, Y-J. Huang (2012). Op imizing he In elligen Ele a o G oup Con ol Sys em by Using Gene ic Algo i hm. Ad anced Science Le e s 9(1), pp. 957-962 [6] L.d.S. Coelho (2010) Gaussian quan um-beha ed pa icle swa m op imiza ion app oaches o cons ained enginee ing design p oblems, Expe Sys ems wi h Applica ions , 37(2), pp.1676-1683. [7] P. Co es, J. La añe a, L. Onie a, (2003) A gene ic algo i hm o con olling ele a o g oup sys ems. In: A i icial Neu al Ne s P oblem Sol ing Me hods , Pa II Vol. 2687, pp, 313-320. [8] P. Co és, J. La añe a, L. Onie a (2004) Gene ic algo i hm o con olle s in ele a o g oups: analysis and simula ion du ing luncpeak a ic, Applied So Compu ing , 4(2), pp.159-174. [9] P. Co és, J. Muñuzu i, L.Onie a (2006) Design and analysis o a ool o planning and simula ing dynamic e ical anspo , Simula ion: T ansac ions o he Socie y o Modelling and Simula ion In e na ional 82(4), pp. 255-274. [10] P. Co és, J. Fe nández, J. Guadix and J. Muñuzu i (2012) Fuzzy logic based con olle o peak a ic de ec ion in ele a o sys ems, Jou nal on Compu a ional and Theo e ical Nanoscience 9 (2), pp. 310-318. [11] M. Du sun (2010). Es ima ion o passenge wai ing ime in ele a o sys ems wi h a i icial neu al ne wo k. In elligen Au oma ion and So Compu ing 16(1), pp. 101-110 [12] J. Echa a ia and C. M. F enz (2009). Imp o ing Ele a o Call Time Responsi eness ia an A i icial Neu al Ne wo k Con ol Mechanism. Sys ems, Applica ions and Technology Con e ence , 2009. LISAT '09. IEEE Long Island, pp. 1-3. [13] M.Z. Hasan, R. Fink, M.R. Suyambu, M.K. Baska an (2012). Assessmen and imp o emen o in elligen con olle s o ele a o ene gy e iciency. IEEE In e na ional Con e ence on Elec o/In o ma ion Technology , EIT 2012; Indianapolis, (Code 91401). [14] K. Hi asawa, T. Eguchi, J. Zhou, L. Yu, J. Hu, S. Ma kon (2008) A double-deck ele a o g oup supe iso y con ol sys em using gene ic ne wo k p og amming, IEEE T ans. Sys ., Man, Cybe n. C, Appl. Re ., 38(4), pp. 535–550. [15] C.E. Im ak (2008) A i icial neu al ne wo ks applica ion in duplex/ iplex ele a o g oup con ol sys em. Jou nal o Mechanical Enginee ing 54 (2), pp. 103-114. [16] C.E. İm ak and G.C. Ba ney (2001) The Applica ion o neu al ne wo ks o li a ic con ol, Ele a o Wo ld, 49(5), p. 82. [17] J. Jamaludin, N.A. Rahim, W.P. Hew (2010) An ele a o g oup con ol sys em wi h a sel - uning uzzy logic g oup con olle , IEEE T ansac ions on Indus ial Elec onics 57 (12), pp. 4188-4198 [18] J. Kennedy, R.C. Ebe ha (1995) Pa icle swa m op imiza ion. In: P oc. IEEE In 'l. Con . on Neu al Ne wo ks, Vol. IV, 1942–1948. Pisca away, NJ: IEEE Se ice Cen e . [19] C. B. Kim, K. A. Seong, H. L. Kwang, and J. O. Kim (1998) Design and implemen a ion o a uzzy ele a o g oup con ol sys em, IEEE T ans. Sys ., Man, Cybe n. A, Sys ., Humans, 28(3), pp. 277–287. [20] Z. Li, (2010) PSO-Based eal ime scheduling o ele a o g oup supe iso y con ol sys em, In elligen Au oma ion and So Compu ing, 16(1), pp.111-121. [21] Z. Li,, H-Z.Tan, Y-N. Zhang, Z-Y. Mao (2007). Dynamic op imiza ion o ele a o g oup con ol based on a i icial immune algo i hm o in e - loo peak a ic du ing lunch- ime, Con ol Theo y and Applica ions 24(2), pp. 177-182. [22] T. Tyni, J. Ylinen (2006) E olu iona y bi-objec i e op imisa ion in he ele a o ca ou ing p oblem. Eu opean Jou nal o Ope a ional Resea ch 169, pp. 960–977