Optimization of Light Switching Pattern on Large Scale using Genetic Algorithm

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1 Optmzato of Lght Swtchg Patter o Large Scale usg Geetc Algorthm Pryaka Sambyal, Pawaesh Abrol 2, Parvee Lehaa 3,2 Departmet of Computer Scece & IT 3 Departmet of Electrocs Uversty of Jammu, Jammu, J&K, Ida Emal address: pryaka46778@gmal.com, 2 pawaesh.abrol@gmal.com, 3 pklehaa@gmal.com Abstract Geetc Algorthm (GA) s a optmzato techque that s based o the prcple of atural evoluto. It performs effcet search global spaces to get a optmal soluto. Optmzato s carred out through atural exchage of geetc materal betwee parets.ga has bee wdely used to solve varous optmzato problems. Ths paper aalyses optmzato of lght patters usg geetc algorthm. Varous parameters that affect optmzato of lght patters have bee desged. Depedg upo the parameters a objectve fucto s formulated that helps the evaluato of optmal soluto. The overall objectve of the paper s to fd out the optmal lght patter that meets the gve costrats. A aalyss of the effect of umber of geeratos o the objectve fucto s also performed. The algorthm satsfactorly results optmzato of lght patter successve teratos. Keywords Costrats; Geetc algorthm; Lght swtchg patter; Objectve fucto. I. INTRODUCTION Geetc Algorthm s a optmzato techque that mtates the process of atural evoluto. It s based o the Darw theory of atural evoluto The survval of the fttest [], [2]. It s a powerful techque for spectg a large soluto space. The term Geetc algorthm was frst coed by Joh Hollad 960 s [3]. The atural exchage of geetc materal betwee parets results optmzato. Parets geerates ew off sprgs. The ftess of each dvdual s evaluated ad the fttest dvduals are allowed to survve. I computer world, strgs of bts represet geetc materal ad procedure of atural selecto s replaced by ftess fucto. Ftess fucto s used to determe the ftess of each dvdual soluto [4]. Geetc algorthm s a heurstc search optmzato techque that results the optmzato of the problem. Optmzato s the process of fdg the soluto wth best performace uder the gve costrats by maxmzg the desred factors ad mmzg the udesred oes. Optmzato problems are the set of problems that provdes the best results ether by maxmzg the proft or mmzg the effort. There are varous optmzato techques such as Hll clmbg whch s based o local search. I Hll clmbg techque a startg pot s selected ad successvely the ext closer pots are selected ad fally a goal pot s acheved. Aother optmzato techque s At coloy whch mtates the behavor of ats. Itally, all ats search for food arbtrary drecto ad oce path s foud tha all ats move o that path. Smulated aealg s aother optmzato techque that yelds the optmzed results. Geetc algorthm s also a optmzato techque that yelds best optmzed results.ga frstly selects the dvdual to produce ext geerato ad the mapulates the selected dvdual to produce ext geerato [5]. Ftess of every possble soluto s calculated. Geetc Algorthm dffers from other optmzato techques may ways such as GA searches parallely for soluto whch prevets t to stay local soluto, GA uses ecoded chromosome rather tha parameters tself ad GA supports mult objectvty [6]. Geetc algorthm s wdely used because t yelds accurate results ad has fast processg tmes may applcatos such as mage processg [2], optmzato of lghtg desg [7], home automato [8], electroc crcut desgg, wreless sesors, trag artfcal eural etworks, automated desgs of room, multcrtera optmzato problems [9], automato of street lghts, traffc cotrol systems ad pedestra crossg [0], securty systems, clusterg, computer automated desgs, flterg ad sgal processg, power electroc desgs [], optmzato of electrcal dstrbuto etwork [2],schedulg applcatos [3] ad may more. I recet years, t had bee see that lghtg systems takes cosderable amout of the global electrcty cosumpto ad also the total cost volved s very hgh. The total electrcty cosumpto ca be reduced by usg dfferet lght patters at dfferet tmes. Lght swtchg patters are bascally the dfferet patters of lght that ca be geerated for dfferet evets. Mostly the lght systems are fully operated at ght tme. But durg ght tme there ca be certa evets depedg upo whch lght s requred. For example a uversty durg late ght there s less requremet of lght tha at that tme a dfferet patter of lght ca be adopted that may results reducto of eergy cosumpto. Optmzato of lght swtchg patters ot oly reduces total eergy cosumpto but also results costs savgs. Optmzato of lghtg systems ca be acheved o large scale by developg swtchg patters other tha statc lght patter developed by electrcal wrg. 9 Algorthm,, Volume 3, Issue, pp. 9-23, 207.

2 I ths paper, Geetc Algorthm has bee appled to optmze lght swtchg patter. Varous parameters are cosdered for ths optmzato problem cludg area of llumato of a pole, wattage of pole, prorty of a pole ad dstrbuto of poles. The overall objectve s to optmze all these parameters to geerate a lght patter. Further, the formulato of cost fucto s doe whch s based o these parameters. The effcecy of geetc algorthm s greatly affected by objectve fucto [4]. So, objectve fucto s formulated very carefully. The effect of umber of geeratos o objectve fucto s also vestgated. The outle of the paper s as follows: Secto II gves overvew of geetc algorthm. Secto III gves aalyss of proposed methodology ad hghlghts the techque form of flowchart. Expermetal results ad performace evaluato are covered Secto IV. Cocluso of the paper s gve Secto V. II. GENETIC ALGORITHM Geetc algorthm s used to solve varous optmzato problems where soluto space s very large. Ay smple geetc algorthm terates through fve basc steps for fdg a optmal soluto [5]. The basc steps of geetc algorthm are show Fg... Start Defe parameters ad cost fucto Geerate tal populato Ftess evaluato. Italzato: To start wth geetc algorthm a tal populato of DNA must be geerated. Each DNA represets soluto to problem. 2. Evaluato: I ths step each dvdual soluto s evaluated. The evaluato s carred out usg objectve fucto. Objectve fucto evaluates the ftess of each dvdual ad determes the fttest dvdual. 3. Selecto: Ths procedure cludes selecto of best dvduals for crossover. The ftess value of all dvduals s cosdered ad the best dvduals are selected as parets. 4. Crossover: After selecto process, the selected parets are allowed to perform crossover to geerate ew dvduals. The crossover s performed by selectg crossover pot. The two paret chromosomes are crossed at crossover pot. 5. Mutato: I order to prevet solutos to get trapped local optma, mutato s performed. Mutato allows the soluto to explore ew soluto space areas. Mutato allows the process to chage some bts the soluto. III. PROPOSED METHODOLOGY I ths paper, a attempt has bee made to fd the optmzed lght patter usg geetc algorthm. The proposed methodology s show the form of flow chart Fg. 2. Followg steps have bee performed to acheve ths objectve. A. Parameter Italzato The objectve fucto s govered by fve parameters. These parameters are desged to be used objectve fucto for calculatg the ftess of each soluto. The fve parameters are: A : Area llumated by th pole TA: Total area llumated by all poles D j: Sum of the dfferece betwee poles j th DNA PR : Prorty gve to th pole S : Status of th pole( for ON ad 0 for OFF) E : Eergy cosumed by th pole Selecto Crossover Mutato Termato crtero met? Stop Fg.. GA Flowchart. B. Populato Italzato A tal populato of radom DNA s s geerated. These DNA s are ecoded bary represetato. Each DNA cossts of gees g, g 2, g 3, g 4..g. The value of a gee ca be ether 0 or whch represets the status of a pole. 0 represets the pole s OFF state ad represet the pole s ON state. C. Calculate Ftess fucto The ftess of each DNA s calculated usg the ftess fucto F j whch s defed as: F j = A TA * D j * S * E PR 20 Algorthm,, Volume 3, Issue, pp. 9-23, 207.

3 Where: Fj s the ftess value of j th DNA, A s the area llumated by th pole, TA s the total area llumated by all poles, D j s the dfferece betwee cosecutve poles j th DNA, PR prorty gve to h pole, S s the status of th pole ad E s the eergy cosumed by h pole. start Italze Parameters Geerate tal radom populato of N DNAs Calculate ftess fucto for every DNA Ftess calculated for each DNA? Sort DNAs descedg ftess values Mate DNA wth oe radom DNA obtaed from postos 2 to N ad replace last ad secod last DNA wth ew DNAs Mutate a radom DNA from postos N- ad N Termato crtero met? stop Step=step+ Fg. 2. Flow chart of proposed methodology. D. Sort the ftess fucto F j descedg order The obtaed value of ftess fucto for dfferet DNA s s sorted descedg order. E. Arrage the DNA s accordg to the sorted ftess fucto The DNAs are the arraged accordg to ther ftess values so that the frst DNA represets the fttest DNA. Ths DNA populato wll ow be used further steps. F. Matg Matg s carred out betwee two DNAs. For matg two paret DNAs are requred. The frst DNA the DNA populato represets frst paret Paret ad the secod paret.e. Paret 2 wll be selected radomly from postos 2 to N. Paret ad Paret 2 are represeted as: Paret = g g, g... g, 2, 3, g 2,, g3 g... g Paret 2= A radom posto for crossover s geerated. Ths radom posto s also called crossover pot. The DNAs are splced ad the ew off sprgs geerated are represeted as: O = Paret :, Paret2 : O 2= Paret :, Paret : 2 O represets frst offsprg ad O 2 represet secod off sprg. Now, place the ew off sprgs place of DNA N- ad DNA N. The ew DNA populato cossts of prevous gees from DNA to DNA N-3 ad the last two DNAs are replaced by ew off sprgs. G. Mutato Mutate a radom DNA through posto N- ad N that represets ew off sprgs. Ay radom posto selected DNA s geerated ad the bt posto s mutated. H. Go to step 2 ad repeat The algorthm s repeated to a umber of geeratos that are predefed. Whe the stoppg crtero s reached the algorthm termates ad the output obtaed wll be the fal output. I order to test ad aalyse the algorthm we have surveyed a actual locato ad data related to poles such as area of llumato of poles, wattage of poles ad physcal dstrbuto of poles have bee collected. Each pole s assged a prorty depedg upo varous factors lke securty, physcal dstrbuto etc. The value of the prorty rages from 0 to.e. from hghest prorty to lowest prorty. The data s collected for 25 poles. For smplcty three types of poles are cosdered P, P2 ad P3 wth area of llumato as0m, 20m ad 30m respectvely ad wattage as 50W, 70W ad 00W respectvely. Geetc Algorthm s appled o the collected data to produce optmzed patter. Aalyss of dfferet parameters s doe for dfferet teratos. Thus, based o the data collected a optmal patter had bee geerated usg geetc algorthm. 2 Algorthm,, Volume 3, Issue, pp. 9-23, 207.

4 IV. RESULTS AND DISCUSSION A tal populato of 0 DNA s s geerated for settg up geetc algorthm. Each DNA cossts of 25 gees that represet the umber of poles. The vestgatos are carred for dfferet teratos, 5, 0, 50, 00, 250, 500 ad 000. Fg. 3. Effect of successve teratos o objectve fucto. The value of the objectve fucto at dfferet teratos s show Fg. 3. The value of objectve fucto becomes stable after 000 geeratos. So, 000 geeratos s cosdered as the stoppg crtero. TABLE I. Effect of successve teratos o objectve fucto. Iterato umber Value of objectve fucto Table I shows the varato of objectve fucto at dfferet teratos. The value of objectve fucto s evaluated from dfferet parameters.e. area covered, umber of ON poles, total eergy cosumed ad the sum of prortes of poles patter. As the talzato s radom therefore, the geerated output may vary. The objectve fucto depeds upo varous factors. Some factors are maxmzg the cost fucto such as percetage of area llumated, sum of prortes a patter ad the physcal dstrbuto of poles a patter ad some are mmzg the cost fucto amely umber of poles llumated a patter ad eergy cosumed by a patter. TABLE II. Effect of umber of teratos o Parameters. Iterato o. Area (%) No. of ON poles Eergy Effcecy (%) Sum of prortes Table II represets value of dfferet parameters at teratos, 5, 0, 50, 00, 250, 500 ad 000. For terato the percetage of area llumated s 50 per cet whch s qute low. The value of sum of prortes s 60 whch dcate that the 9 bulbs that are llumated are of low prorty. Though, eergy effcecy s 54 per cet but the patter obtaed cotas poles wth low prortes. For terato umber 5 the percetage of area llumated creases to 56 per cet ad the value of sum of prortes s 63 whch s better tha prevous value. Thus, movg from terato 5 to terato 000 the value of area ad sum of prortes s creasg. For 000 terato umber the area llumated s 73 per cet ad sum of prortes s 83. The values obtaed dcate that 73 per cet of the area s llumated wth 4 poles ad the eergy effcecy s 30 per cet. The fal patter obtaed cosst most of the poles wth hgh prorty. So, the lght patter obtaed llumates all the poles wth hgh prortes resultg the optmzato of poles requred to llumate the area ad the eergy effcecy. Fg. 4 shows the graphcal represetato of the value of parameters at dfferet teratos. Fg. 4. Effect of successve teratos o Parameters. 22 Algorthm,, Volume 3, Issue, pp. 9-23, 207.

5 V. CONCLUSION Geetc algorthm s a powerful optmzato techque that geerates best optmal solutos for varous problems. I ths paper a lght patter optmzato problem s cosdered ad vestgatos are carred out to geerate the optmal lght patter. Desgg of varous parameters that affect the optmzato of lght patter s performed. Fve parameters are cosdered for optmzato of lght patter amely area llumated by patter, umber of ON poles patter, eergy cosumed by patter, dstrbuto of poles a patter ad the prorty gve to a pole. To optmze these parameters a objectve fucto s formulated. To cover all the aspects of the problem, objectve fucto s carefully formulated. Expermetato s performed o a area wth three dfferet types of poles. Dfferet types of poles have dfferet wattage ad tedecy to llumate the area. The algorthm s the mplemeted ad the results are aalysed for dfferet umber of geeratos. The results obtaed are sgfcat. The fal patter obtaed results llumato of area wth less umber of poles. REFERENCES [] Chaahat, M. Bahl, P. Lehaa ad S. Kumar, Image Brghtess Ehacemet of Natural ad Uatural Images usg Cotuous Geetc Algorthm, Iteratoal Joural of Advaced Research Computer Scece ad Software Egeerg, vol. 3, ssue 9, September 203. [2] R. Garg ad S. Mttal, Optmzato By Geetc Algorthm, Iteratoal Joural of Advaced Research Computer Scece ad Software Egeerg, vol. 4, ssue 4, Aprl 204. [3] P.K. Yadav ad Dr. N.L. Prajapat, A Overvew of Geetc Algorthm ad Modellg, Iteratoal Joural of Scetfc ad Research Publcatos, vol. 2, ssue 9, September 202. [4] R. Mahaja, A. Kumar ad P. Lehaa, Image brghtess Ehacemet of Vsble ad Ifrared Images usg Geetc Algorthm, Iteratoal Joural of Scetfc ad Techcal Advacemets, vol., ssue, pp. 25-3, 205. [5] G. Josh, Revew of Geetc algorthm: A Optmzato techque, Iteratoal Joural of Advaced Research Computer Scece ad Software Egeerg, vol. 4, ssue 4, Aprl 204. [6] A. Sharam, R. P. Sgh ad P. Lehaa, Evaluato of the Accuracy of Geetc Algorthms Oretato Estmato of Objects Idustral Evromet, Iteratoal Joural of Scetfc ad Techcal Advacemets, vol., ssue 4, pp. 7-4, 205. [7] F.M. Lma, I.S. Peretta ad A. Cardoso, Optmzato of Lghtg Desg Usg Geetc Algorthm, IEEE Xplore, December 200. [8] A. Srvastava ad Dr. K.M. Sgh, A Smart Home Automato System Usg Adrod Phoe, Iteratoal Joural of Scetfc Research, vol. 4, ssue 0, October 205. [9] F. Perodet, H. Lahmd ad P. Mchel, Use of Geetc Algorthm for Multcrtera Optmzato of Buldg Refurbshmet, Eleveth Iteratoal IBPSA Coferece, Scotlad, pp , July [0] A.M. Turky, M.S. Ahmad ad M. Z. M. Yusoff, The Use of Geetc Algorthm for Traffc Lght ad Pedestra Crossg Cotrol, Iteratoal Joural of Computer Scece ad Network, vol. 9, o.2, February [] A.G Bakrtzs, P.N. Bskas, C.E. Zoumas ad V. Petrds, Optmal Power Flow by Ehaced Geetc Algorthm, IEEE trasactos o power systems, vol. 7, o. 2, May [2] J.Z. Zhu Optmal recofgurato of electrcal dstrbuto etwork usg the refed geetc algorthm, Elsever Scece, vol. 62, Jauary [3] Avalable: c_algorthm_applcatos&gr=4fmnfb&hl=e-in. [4] H. Kour, P. Sharma ad P. Abrol, Aalyss of ftess fucto geetc algorthms, Joural of Scetfc ad Techcal Advacemets, vol., ssue 3, pp , 205. [5] K. F. Ma, K. S. Tag, ad S. Kwog, Geetc Algorthms: Cocepts ad Applcatos, IEEE Trasactos o Idustral Electrocs, vol. 43, o. 5, October Algorthm,, Volume 3, Issue, pp. 9-23, 207.