Bin Bloom Filter Using Heuristic Optimization Techniques for Spam Detection

Transcription

1 World Academy of Scence, Engneerng and Technology Internatonal Journal of Computer and Informaton Engneerng Bn Bloom Flter Usng Heurstc Optmzaton Technques for Spam Detecton N. Arulanand, K. Premalatha Internatonal Scence Index, Computer and Informaton Engneerng waset.org/publcaton/ Abstract Bloom flter s a probablstc and memory effcent data structure desgned to answer rapdly whether an element s present n a set. It tells that the element s defntely not n the set but ts presence s wth certan probablty. The trade-off to use Bloom flter s a certan confgurable rsk of false postves. The odds of a false postve can be made very low f the number of hash functon s suffcently large. For spam detecton, weght s attached to each set of elements. The spam weght for a word s a measure used to rate the e-mal. Each word s assgned to a Bloom flter based on ts weght. The proposed work ntroduces an enhanced concept n Bloom flter called Bn Bloom Flter (BBF). The performance of BBF over conventonal Bloom flter s evaluated under varous optmzaton technques. Real tme data set and synthetc data sets are used for expermental analyss and the results are demonstrated for bn szes 4, 5, 6 and 7. Fnally analyzng the results, t s found that the BBF whch uses heurstc technques performs better than the tradtonal Bloom flter n spam detecton. Key Cuckoo search algorthm, levy s flght, metaheurstc, optmal weght. I I. INTRODUCTION NTERNET traffc has been growng at a tremendous pace and varous applcaton servces are becomng ndspensable n day-to-day lfe. To keep up wth the growng demand, the nternet must have hgh performance, sophstcated packet control functonalty. One such popular applcaton s the use of e-mal. E-mals are extensvely used as a mode of communcaton. From the begnnng of the Internet, sendng of junk e-mal or spam e-mal has been prohbted. However, spam e-mals have become ndspensable and have steadly grown snce the early 1990s. There are varous methods developed to detect and flter the unsolcted spam e-mals. Spammng s the acton of sendng unsolcted commercal messages n bulk wthout the explct permsson or desre of the recpents. That s spam s an rrelevant or napproprate messages sent on the nternet to a large number of users. Everyday bllons of e-mals are beng passed around and that many spam e-mals are also sent. A lot of tme s wasted tryng to get rd of spam from the computer and spam also takes up desrable memory space. A spam flter works by scannng all ncomng e-mals and evaluatng them for sgns of spam such as specfc key, llegtmate lnks, etc. There are dfferent types of spam flters. Some are pre-programmed to trace known spammers and others flter e-mals based on the that are wrtten n the body of the e-mal. For spam detecton, an effcent data structure s needed to test the Arulanand N. s wth the PSG College of Technology, Combatore, Taml Nadu, Inda (e-mal: arulnat@yahoo.com). membershp of n the e-mals. A Bloom flter s a compact data structure desgned to store queres that help to establsh set membershp nformaton. A Bloom flter s a space-effcent randomzed data structure for representng a set n order to support membershp queres [1]. Bloom flters allow false postves but t saves space. It balances the drawback when the probablty of an error s made adequately low. The bloom flter s very popular n database applcatons and networkng. In ths work, a weght s assgned for each word appears n e-mals based on ts frequency n both spam and legtmate mals [2]. Ths weght value ndcates ts probable belongngs to spam or legtmate. In the frst step of spam flterng each word s assgned by a weght. Bloom flter treats every word equally and t allocates the same number of hash functons to every word whch results n hgh total membershp nvaldaton cost. The proposed work ntroduces an enhanced concept n Bloom flter named as Bn Bloom Flter (BBF) whch groups the nto a number of Bloom flters whch wll have dfferent false postve rates based on the weghts of the spam. Instead of gvng equal mportance to all the n a spam word lst, dfferent mportance s gven based on ther weghts. The wth hgh weghts are stored n a Bloom flter wth lowest false postve rate and the wth low weghts are stored n a Bloom flter wth hgh false postve rate. For a hgh cost bn t s expected to allocate more hash functons to decrease the membershp nvaldaton rate, and conversely, a low cost bn allows hgher false postve probablty, and then the BBF makes the total membershp nvaldaton cost a mnmum. In tradtonal Bloom flter, every word s treated equally and t allocates the same number of hash functons to every word whch results n hgh total membershp nvaldaton cost. For ths process, the number of to be stored n the ndvdual Bloom flter and the number of Bloom flters are dentfed to reduce the total membershp nvaldaton cost for a gven collecton of wth ther weghts. The number of possble solutons n the search space s so large. So t s an optmzaton problem to dentfy the number of to be stored, false postve rate and number of hash functons of each Bloom flter n the Bn whch mnmze the total membershp nvaldaton cost. It prevents an exhaustve search for the best answer. Ths paper compares the optmzaton of bloom flter n spam flterng usng Genetc Algorthm (GA), Partcle Swarm Optmzaton (CSA), Clonal Selecton Algorthm (CSA), Cuckoo Search (CS) and Bat algorthm. The organzaton of ths paper s as follows. Secton II deals wth the Bloom flter. Internatonal Scholarly and Scentfc Research & Innovaton 8(8)

2 (World Academy of Scence, Engneerng and Technology Internatonal Journal of Computer and Informaton Engneerng Internatonal Scence Index, Computer and Informaton Engneerng waset.org/publcaton/ Secton III presents the Bn Bloom Flter (BBF). Secton IV dscusses the BBF usng heurstc optmzaton technques. Performance evaluaton of heurstc optmzaton technques for the BBF s reported n Secton V. II. BLOOM FILTER The base data structure of a Bloom flter s a bt vector of sze m. Gven a strng X the Bloom flter computes k hash functons on t producng k hash values rangng from 1 to m. It then sets k bts n an m-bt long vector at the addresses correspondng to the k hash values. The same procedure s repeated for all the members of the set. Ths process s called programmng of the flter. The query process s smlar to programmng, where a strng whose membershp s to be verfed s nput to the flter. The Bloom flter generates k hash values usng the same hash functons t used to program the flter. The bts n the m-bt long vector at the locatons correspondng to the k hash values are looked up. If at least one of these k bts s not found n the set then the strng s declared to be a nonmember of the set. If all the bts are found to be set then the strng s sad to belong to the set wth a certan probablty. Ths uncertanty n the membershp comes from the fact that those k bts n the m-bt vector can be set by any other n-1 members. Thus fndng a bt set does not necessarly mply that t was set by the partcular strng beng quered. However, fndng a bt not set certanly mples that the strng does not belong to the set. Fg. 1 shows the structure of Bloom flter. The false postve rate of Bloom flter s gven n equaton (1). f = (1-e -kn/m ) k (1) Fg. 1 Bloom Flter III. BIN BLOOM FILTER Consder a set S{s 1,s 2,...,s n } of n elements. Bloom flter depcts membershp Informaton of S usng a bt vector B of length m for the k hash functons, h 1, h 2,..., h wth k h : v {1..m}. A Bn Bloom Flter s a varant of Bloom flter whch s used to store the that have dfferent spam weghts. Instead of gvng equal mportance to all the n a spam word lst, the mportance s gven based on ther weghts. The wth hgh weghts are stored n a Bloom flter wth lowest false postve rate and the wth low weghts are stored wth hgh false postve rate. A Bn conssts of a number of Bloom flters and each Bloom flter has dfferent tuple <n,f,w> whch causes dfferent membershp nvaldaton cost. Fg. 2 shows BBF wth ts tuple <n,f,w> confguraton where n, f and w respectvely represents the number of strngs, false postve rate and average weght of strngs n Bloom flter and L represents the number of Bloom flters n a bn. Fg. 2 Bn Bloom Flter For membershp testng the total cost of the set s the sum of the nvaldaton cost of each subset. The total membershp nvaldaton cost s gven as, F = n (2) 1f1w1 +n2f2w n fw +...nl flwl The total membershp nvaldaton cost s to be mnmzed; where L = 1 F(L) = n f w (3) L 1 n N and n l n n u N- Total number of strngs n a spam set n l Lower lmt for number of strngs to be stored n a Bloom flter n u Upper lmt for number of strngs to be stored n a Bloom flter f ln2 1 2 r m n r j j j 1 rlnf= (1 L) Snce Bloom Flter allows false postve, the membershp nvaldaton cost s unavodable [3]. For BBF, the total membershp nvaldaton cost s expressed n (3). Havng dfferent weghts n dfferent Bloom flters nto consderaton, the total membershp nvaldaton cost for Bloom flter s then as follows: F standard (L) f L n w 1 L ln2 m 1 j1 n j L F standard(l) nw (5) 1 2 (4))Internatonal Scholarly and Scentfc Research & Innovaton 8(8)

3 ()ma()(())mamaworld Academy of Scence, Engneerng and Technology Internatonal Journal of Computer and Informaton Engneerng Internatonal Scence Index, Computer and Informaton Engneerng waset.org/publcaton/ The average false postve rate of the last teraton of BBF s taken as the false postve rate of Bloom flter. IV. HEURISTIC OPTIMIZATION TECHNIQUES FOR BBF The proposed work apples varous heurstc optmzaton technques n BBF for dentfyng the mnmal total membershp nvaldaton cost for dfferent bn szes and the results are compared wth Bloom flter. A. Genetc Algorthm The GA begns wth an ntal populaton of the problem [4]. Ths algorthm encodes a potental soluton to a specfc problem on a smple chromosome and t apples crossover and mutaton operators to these structures. Each one of these solutons must be evaluated by means of a ftness functon. The goodness of a soluton s typcally defned wth respect to the current populaton.ga mmcs the survval-of-the-fttest prncple of nature to make a Search process. Therefore, GA s naturally sutable for solvng maxmzaton problems. Mnmzaton problems are usually transformed nto maxmzaton problems by sutable transformaton. For mnmzaton problems, to generate non-negatve values n all the cases and to reflect the relatve ftness of ndvdual strng, t s necessary to map the underlyng natural objectve functon to ftness functon. The objectve functon f(l) taken as a standard for the problem of mnmzaton s CFLfFLCfLx x 0fFLC(6) xfg. 3 Encodng Soluton for BBF The transformaton from mnmzaton to maxmzaton does not alter the locaton, but t converts a mnmzaton problem nto an equvalent maxmzaton problem. In the perspectve of BBF, a chromosome represents a number of Bloom flters wth ts, false postve rate and average spam weght of the. Explctly, each chromosome X, s constructed s shown n Fg. 3 where n j, f j and w j refer respectvely the number of, false postve rate and the weght of the j th Bloom flter of th chromosome. A set of 3 genes <n,f,w> encodes a proten a trat, that s a sngle Bloom flter of a bn. B. Clonal Selecton Algorthm In CSA antgen refers to the problem and ts constrants [5], [6]. The algorthm performs the selecton of antbodes based on affnty ether by matchng aganst an antgen pattern or va evaluaton of a pattern by a cost functons. In clonal selecton n, the hghest ftness antbodes are selected for clonng wth the rate of β. The amount of clones to be generated for all these n selected antbodes s gven n (7): n β P round 1 N C (7) where N C s the total amount of clones generated, β s a multplyng factor whch decdes number of clones to be produced, P s the total amount of antbodes and round() s the operator that rounds ts argument towards the closest nteger. The Cauchy mutaton operator s appled for mutaton. In the perspectve of BBF, an antbody represents number of Bloom flters wth number of, false postve rate and average spam weght of the [7]. The representaton of each antbody X s shown n Fg. 3. An antbody n the populaton represents one possble soluton for assgnng the trples <n, f, w> for L Bloom flters. Intally each antbody randomly chooses dfferent <n, f, w> for L bns. The ftness functon of partcle s calculated usng (6) C. Partcle Swarm Optmzaton PSO algorthm s an optmzaton technque, usng stochastc search guded by the prncples of collectve behavour and self-organzaton of flock of brds [8], [9]. In PSO, each sngle soluton s lke a brd n the search space, whch s called partcle. It s ntalzed wth a populaton of random solutons. All partcles have ftness values whch are evaluated by the ftness functon to be optmzed. Each partcle n PSO s assocated wth a velocty. Partcles fly through the search space wth certan veloctes whch are dynamcally adjusted accordng to ther hstorcal behavor [10]. In the framework of BBF, an ndvdual partcle representaton s shown n Fg. 3. One partcle n the swarm represents one possble soluton of BBF. Therefore, a swarm represents a number of canddate solutons for the current data vectors. At the ntal stage, each partcle randomly chooses dfferent <n, f, w> for L Bns. The ftness functon of partcle s calculated usng (4). The BBF s optmzed usng PSO wth three types of velocty equatons by ncorporatng statc nerta weght, determnstc nerta weght and constrcton factor [11]. D. Cuckoo Search CS s an optmzaton technque developed by Yang and Deb based on the oblgate brood parastsm of cuckoo speces by layng ther eggs n the nests of other host brds [12]. If a host brd dscovers the eggs whch are not ts own, t wll ether throw these foregn eggs away or smply abandon ts nest and buld a new nest elsewhere. Each egg n a nest represents a soluton, and a cuckoo egg represents a new soluton. The better new soluton (cuckoo) s replaced wth a soluton whch s not so good n the nest. In the smplest form, each nest has one egg [13], [14]. When generatng new solutons for a cuckoo, a Levy flght s performed. Bascally Levy flghts provde a random walk whle ther random steps are drawn from a Levy dstrbuton for large steps gven n (8): Levy ~ u t λ (8) Internatonal Scholarly and Scentfc Research & Innovaton 8(8)

4 World Academy of Scence, Engneerng and Technology Internatonal Journal of Computer and Informaton Engneerng Internatonal Scence Index, Computer and Informaton Engneerng waset.org/publcaton/ whch has an nfnte varance wth an nfnte mean. E. Enhanced Cuckoo Search The Enhanced Cuckoo Search (ECS) s an extended verson of CS wth dfferent types of host nest wth multple eggs. The cuckoo selects three types of nest for layng ther eggs [15], [16]. 1. Common cuckoo selects a group of host speces wth smlar nest stes and egg characterstcs to her own. There s a sgnfcant level of smlarty between cuckoo eggs and typcal eggs of the host speces. 2. Some hosts do not exhbt egg rejecton behavour and the cuckoo eggs look very dssmlar from the host eggs. 3. Other speces of cuckoo lay cryptc eggs, whch are dark n color when the eggs of ther hosts are lght. Ths s a trck to hde the egg from the host that parastzes hosts wth dark nests. In the envronment of BBF, an egg represents a number of bloom flters wth ts number of to be stored, false postve rate and average spam weghts of the. The ftness functon for CS and ECS s calculated usng (4). The representaton egg s shown n Fg. 3. In ECS each nest contans 4 eggs. Three dfferent types of eggs are produced by applyng Levy flght, Unform mutaton and Random mutaton on current soluton. F. Bat Algorthm Yang proposed bat algorthm for optmzaton. Bats use echolocaton to locate and catch ther prey [17]. When bats fly, they produce a constant stream of hgh-ptched sounds that only bats are able to hear. Ther pulses vary n propertes and can be correlated wth ther huntng strateges, dependng on the speces. The loudness also vares from the loudest to a queter base when searchng for prey and homng towards prey respectvely. The velocty and poston of the bat s gven by f f (f f ) β mn max mn V (t) (9) V (t -1) (X (t) X * ) f (10) (t) X X (t 1) V (t) (11) where β[0,1] s a random drawn from a unform dstrbuton. X * s the current global best locaton among n bat solutons. For the local search, once a soluton s selected among the current best solutons, a new soluton for each bat s generated locally usng random walk. A (t+1) = αa (t) (13) r (t+1) = r (0)(1-exp(-γt)) (14) where α and γ are constants, where 0 < α < 1 and γ > 0. The representaton of bat s shown n Fg. 3 and the ftness functon of each bat s evaluated usng (4). V. EXPERIMENTAL ANALYSIS The heurstc optmzaton technques are appled for synthetc data sets of 250, 500 and 1000 strngs and real tme data set of 1000 strngs. Table I show the parameters and ther values of BBF. The number of Bloom flters taken for bn sze s 4, 5, 6 and 7 and each Bloom flter sze s For synthetc data set, the weghts of the are generated randomly from to 5. In real tme data set, the spam weghts of the are calculated from 1000 spam e-mal and 1000 non spam e-mals. The spam weght of the ranges from to 5. For the mplementaton of the proposed work Java Developer s Kt s used n Wndows envronment. Table II show the parameters and ther values of optmzaton technques respectvely. TABLE I PARAMETER VALUES OF BBF Number of Data set stored Word weght Mn Max Mn Max Synthetc data set Synthetc data set Synthetc data set Real tme data set Bloom flter sze No. of teratons Fgs. 4-7 show the cost obtaned from BBF for synthetc data sets of 250 strngs, 500 strngs, 1000 strngs and real tme data set of 1000 strngs usng GA, CSA, PSO wth statc nerta weght (PSO1), PSO wth determnstc nerta weght (PSO2), PSO wth constrcton factor (PSO3), CS, ECS and Bat algorthm respectvely. X new X εa t old (12) where s a random number [ 1, 1] and A t s the average loudness of all bats at the tme step t. The loudness A and the rate r of pulse emsson have to be updated accordngly as the teratons proceed. Fg. 4 Cost of BBF for synthetc dataset of 250 strngs Internatonal Scholarly and Scentfc Research & Innovaton 8(8)

5 World Academy of Scence, Engneerng and Technology Internatonal Journal of Computer and Informaton Engneerng Internatonal Scence Index, Computer and Informaton Engneerng waset.org/publcaton/ TABLE II PARAMETERS AND THEIR VALUES FOR DIFFERENT OPTIMIZATION TECHNIQUES Parameter Value Genetc Algorthm Type of selecton Roulette wheel selecton Type of crossover One pont crossover Type of mutaton Unform mutaton Type of evaluaton Eltst selecton Populaton sze 10 Selecton rate 0.5 Crossover rate 0.8 Mutaton rate 1/3L Clonal Selecton Algorthm Populaton sze 10 No. of teratons 10 Clonng rate β 0.5 d 20% of populaton sze Partcle Swarm Optmzaton Populaton sze 10 Number of Iteratons 50 c1 2.1 c max 0.9 mn 0.4 kf Cuckoo Search Number of nests 10 Number of teratons 50 pa Bat Algorthm Number of bats 10 Number of teratons 50 rand(0,1) rand(0,1) γ 0.5 A 0.25 R 0.5 Fg. 5 Cost of BBF for synthetc dataset of 500 strngs Fg. 6 Cost of BBF for synthetc dataset of 1000 strngs Fg. 7 Cost of BBF for real tme dataset of 1000 strngs GA solves the problem through evoluton. In GA, the parameters play an mportant role n gettng better optmzaton results. It s computatonally ntensve and has premature convergence. Most of the tme, t gves better results than CS, ECS and Bat algorthm. CSA s a populaton based stochastc method. CSA operators namely Clonal Selecton and Somatc Hypermutaton lead to better results compared to GA. CSA has qucker convergence compared to GA due to the populaton that always contans hgher affnty measures. It has only a few parameters to fne tune. CS, ECS and Bat Algorthm are nature-nspred heurstc algorthms. CS s less senstve to varaton n tunng parameters. A modfcaton of Cuckoo Search, named as Enhanced Cuckoo Search s presented n ths paper by consderng multple eggs n the nest and dealng wth dfferent types of cuckoo eggs. Expermental results show that ECS gves better performance than CS. In Bat Algorthm, varous parameters lke loudness, velocty, frequency, pulse emsson and wavelength are used. It s dffcult to fne tune varous parameters to get near optmal soluton. CS and ECS gve better performance than Bat algorthm. Internatonal Scholarly and Scentfc Research & Innovaton 8(8)

6 World Academy of Scence, Engneerng and Technology Internatonal Journal of Computer and Informaton Engneerng Internatonal Scence Index, Computer and Informaton Engneerng waset.org/publcaton/ TABLE III PERCENTAGE OF IMPROVEMENT IN BBF OVER BF Data set No. of Strngs Bn sze GA CSA PSO1 PSO2 PSO3 CS ECS Bat Synthetc Real Tme PSO s a popular swarm ntellgence technque. PSO s easy to mplement when compared to other methods, and there are a few parameters to adjust. PSO has faster convergence n fndng near global optmal solutons. Expermental results show that PSO gves better performance compared to other optmzaton technques due to stochastc and determnstc component present n the partcle. Dfferent varants of PSO, namely statc nerta weght, determnstc nerta weght and constrcton factor are expermented n ths paper. PSO wth constrcton factor gves better results for all the confguratons compared to the other PSO varants. Table III shows the percentage of mprovement n BBF over BF usng varous optmzatons for synthetc data sets and real tme data set. VI. CONCLUSION In ths work an optmal confguraton of BBF wth spam word weght s desgned. In BBF dfferent false postve rates are assgned to the Bloom flters dependng on the spam weght of the. Instead tradtonal Bloom flter uses same false postve rate. From the results t s seen that BBF always outperforms the tradtonal Bloom flter. ECS gves better performance than CS and Bat algorthm. The parameters p a and n CS and ECS reman constant. Due to ths the effcency and performance of these algorthms are decreased. The performance of the Bat Algorthm s determned by the parameters loudness, velocty, frequency, wave length and pulse emsson. It s dffcult to fne tune the parameters to get near optmal soluton. GA solves problem wth multple solutons. Most of the tme, t gves better result than CS, ECS and Bat algorthm. In GA t s dffcult to optmze the parameters nvolved and t s computatonally ntensve. It has premature convergence. CSA has qucker convergence due to the populaton that always contans hgher affnty measures. It has only a few parameters to fne tune. PSO s easy to mplement when compared to other methods, and there are a few parameters to adjust. PSO has very faster convergence n fndng near global optmal solutons. REFERENCES [1] B.H. Bloom, Space/tme tradeoffs n hash codng wth allowable errors, Commun. ACM., vol. 13, no. 7, pp , July, [2] M. Abdoh, M. Musa and N. Salman, Detectng Spam by Weghtng Message Words, J. Arts Sc., vol. 1, no. 1, pp. 1-14, Aug [3] K. Xe, Y. Mn, D. Zhang, G. Xe and J. Wen, Basket Bloom Flters for Membershp Queres, n Proc. IEEE Tencon 05, Melbourne, Qld, pp. 1-6, [4] D.E. Goldberg, Genetc Algorthms n Search, Optmzaton and Machne Learnng, Addson-Wesley, Boston, [5] L.N. De Castro and F.J. Von Zuben, Learnng and Optmzaton usng the Clonal Selecton Prncple, IEEE Trans. Evol. Comput., vol. 6, no. 3, pp , Aug [6] J. Tmms and L.N. de Castro, Artfcal Immune Systems: A New Computatonal Intellgence Approach, Sprnger, London, [7] N. Arulanand, P. Swathy Pryadharsn and S. Subramanan Artfcal Immune System for Bloom flter Optmzaton, Int. J. Computer App., vol. 41, no. 8, pp , March [8] J. Kennedy and R.C. Eberhart, Partcle Swarm Optmzaton, n Proc. IEEE Int. Confer. Neural. Networks., Perth, WA, Australa, pp , 1995 [9] R.C. Eberhart and Y. Sh, Partcle swarm optmzaton, developments, applcatons and resources, n. Proc Cong. Evol. Comput., Seoul, Korea. Pscataway, pp , [10] N. Arulanand, S. Subramanan and K. Premalatha Optmzed Bn Bloom Flter for Spam flterng usng Partcle Swarm Optmzaton, European J. Scentfc Research, vol. 68, no. 2, pp , July [11] M. Clerc and J. Kennedy, The partcle swarm: exploson, stablty, and convergence n a mult-dmensonal complex space, IEEE Trans. Evol. Comput., vol. 6, pp , Feb [12] N. Arulanand, S. Subramanan and K. Premalatha An Enhanced Cuckoo Search for Optmzaton of Bloom Flter n Spam Flterng, Global J. comp. Sce. Tech., vol. 12, no. 1, Jan [13] N. Arulanand, S. Subramanan and K. Premalatha A Comparson study of cuckoo-bat search for Optmzaton of Bloom Flter n Spam Flterng, Int. J. Bo-Inspred Comput., vol. 4, no. 2, pp.89-99, June [14] X.S. Yang, and S. Deb, Engneerng optmsaton by Cuckoo search Int. J. Math. Model. Numer. optm., vol. 1, no. 4, pp , Dec [15] C. Moskat, and M. Honza European Cuckoo Cuculus Canorus Parastsm and Host's Rejecton Behavour n a Heavly Parastzed Great Reed Warbler Acrocephalus Arundnaceus Populaton, Int. J. Avan. Sce., vol. 144, no. 4, pp , Sep Internatonal Scholarly and Scentfc Research & Innovaton 8(8)

7 World Academy of Scence, Engneerng and Technology Internatonal Journal of Computer and Informaton Engneerng [16] A. Moksnes and E. Roskaft Egg-Morphs and Host Preference n the Common Cuckoo (Cuculus Canorus): An Analyss of Cuckoo and Host Eggs form European Museums and Collectons, J. Zool., vol. 236, no. 4, pp , Mar [17] X.S. Yang, A New Metaheurstc Bat-Inspred Algorthm, Nature Inspred Cooperatve Strateges for Optmzaton (NISCO 2010), Studes n Computatonal Intellgence, Sprnger Berln, Sprnger, vol. 284, pp.65-74, Aprl, Internatonal Scence Index, Computer and Informaton Engneerng waset.org/publcaton/ Internatonal Scholarly and Scentfc Research & Innovaton 8(8)