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The hybrid evolutionary algorithm for optimal planning of hybrid woban


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The hybrid evolutionary algorithm for optimal planning of hybrid woban

  1. 1. International Journal of Electronics and Communication Engineering & TechnologyAND INTERNATIONAL JOURNAL OF ELECTRONICS (IJECET), ISSN 0976COMMUNICATION ENGINEERING &3, October- December (2012), © IAEME – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue TECHNOLOGY (IJECET)ISSN 0976 – 6464(Print)ISSN 0976 – 6472(Online)Volume 3, Issue 3, October- December (2012), pp. 122-138 IJECET© IAEME: Impact Factor (2012): 3.5930 (Calculated by GISI) © THE HYBRID EVOLUTIONARY ALGORITHM FOR OPTIMAL PLANNING OF HYBRID WOBAN Anwar A. Alsagaf1, Yousef Y. Holba2, Anwar H. Jarndal3 , Gamil R. Salman4 1,2 (Engineering Department of Hodeidah University, Hodeidah, Yemen) 3 (Electrical and Computer Engineering Department of University of Nizwa, Nizwa, Sultanate of Oman) 4 (SGGS college of Engineering and Technology, SRTM University, Nanded, Maharashtra state. India) ABSTRACT In the recent few years, the hybrid network deployment is an important problem, especially, optimal placement problems of WBSs and ONUs in the WOBAN architecture. The optimal placement problems of WBSs and ONUs will play a key role for overall cost optimization of a hybrid network architecture. The challenge is to obtain the global optimal solution, since the objective function is usually high-dimension, highly non-linear, non- convex, and multimodal, where a local optimum is typically not the global optimal solution. The traditional local and global algorithms could trap to a local optimum. Thus, in this paper, we reformulate our problem as multicriteria optimization problem under uncertainty and represent its by using game model. The two hybrid evolutionary algorithms (HEA) are proposed for solving the optimal placement problems of WBSs and ONUs, independently. The results of modeling show that HEA is powerful technique adequate to our proposed model and give good optimal solutions with comparison by other traditional methods. Keywords. Hybrid evolutionary algorithm, Optimal placement problem, Hybrid wireless network, Hill climbing algorithm. 1. INTRODUCTION In the recent few years, the hybrid wireless network deployment is an important problem, especially, optimal placement problems of wireless base stations (WBSs) and optical network units (ONUs) in the Wireless Optical Broadband Access Network (WOBAN) architecture. The optimal placement problems of WBSs and ONUs will play a key role for overall cost optimization of a hybrid wireless network architecture. This problem has generated much research interest and challenge instances have been published in the more literatures [1-9]. These problems belongs to the class of NP-hard optimization problem with multiple and conflicting objectives. The challenge is to obtain the global optimal solution, since the 122
  2. 2. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEMEobjective function is usually high-dimension, highly non-linear, non-convex, and multimodal,where a local optimum is typically not the global optimal solution. The WBSs placementproblem involves selecting base station site locations from a set of feasible candidates, whichare normally located irregularly on the geographical area. The selected sites must beconfigured to provide adequate required maximum service coverage and capacity at thelowest possible financial cost with unsplittable demands, and unknown numbers ofsubscribers. These two conflicting objectives always exist when setting up cellular networkservice, as adding base station to improve coverage inherently increases the total cost ofnetwork. In this paper, we focus on resolving the two separated fundamental stages of cellplanning problems. First stage, we produce cell planning problems in which WBSs locationsare selected on to the geographical area. The next stage we will providing ONUs optimalplacement. Our goal in this study is finding an optimal locations/positions of WBSs andONUs using two hybrid evolutionary optimization technique, satisfy the conflictingobjectives, taking into account the only factors and constraints which have the largest impacton financial cost and service coverage. These factors are considered in details below. Wereformulate our problem as multicriteria optimization problem under uncertainty andrepresent its by using game model presented in [10, 11]. The hybrid evolutionary optimization techniques have successfully been applied tomulticriteria optimization problems. For solving its we will investigate two algorithms basedon combination of global and local search algorithms. The first hybrid evolutionary algorithmbased on combination of multicriteria genetic algorithm(MGA) and hill climbingalgorithm(Topiks-Veinott algorithm ) to solve the WBSs placement problem, when thesecond hybrid evolutionary algorithm based on combination of multicriteria geneticalgorithm(MGA) and hill climbing algorithm( Modified Tornqvist algorithm ) to solve theONUs placement problem. In this paper, we propose and investigate clustering architecture for WOBAN whichhave focused on the integration of WIMAX 4G and cellular technologies. A hybrid WOBAN(referred to as a “hybrid network” here) consists of a wireless network at the front end, and itis supported by an optical access network, viz., the passive optical network (PON) at the backend. The basic architecture (see Fig. 1). Assume that an Optical Line Terminal (OLT) isplaced in Telecom Central Office (CO) and it feeds several ONUs. Thus, from ONU to theOLT/ CO, we have a traditional fiber network; and, from ONUs, end users are wirelesslyconnected, either directly (in a single hop) or through multihop fashion. In a typical hybridnetwork, end users, e.g., subscribers with wireless routers at individual homes, are scatteredover a geographic area. Fig.1. Hybrid optical-wireless broadband access network architecture[4]. Through performance study on the given two data sets, we show that, the two hybridevolutionary algorithms can improve the chances of reaching the global optimum because ofapplying the neighborhood search algorithm based on the dominance cone construction from 123
  3. 3. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEMEeach points and using choice technique based on the most best selection mechanism with themost worst exception function from population pool. This paper is organized as following: in the section 2 we introduce the problemstatement. The section 3 give the problem formulation as multicriteria optimization problemunder uncertainty. The related works and proposed two HEA are considered in the section 4.The result of experiments presented in the section 5. The section 6 give the conclusion of ourpaper. Acknowledgement is presented in the section 7. In section 8 given the references. 2. PROBLEM STATEMENTAn analysis of numerous scientific studies and works aimed at developing and design ofoptimal automatic positioning systems and optimal planning or relocation of some devicessuch as WBSs, wireless sensors(WS), antenna configuration(AC), wireless accesspoints(APs), and automatic transfer machine(ATM), etc in the various telecommunicationnetworks architecture, especially in the hybrid networks. The problem of optimal design andoptimal planning of the WBSs and ONUs in the proposed WOBAN architecture on theterritorial plane is mostly represented in the form of an optimization problem with manyconditions. This traditional formulation cannot be put to an adequate model because ofproblem complexity, multiobjective functionality, multi-crossing of the input-outputparameters of the system optimization and uncertainty in the environment parameters and theno enough information’s about number of subscribers, user demand, end user etc. Therefore itis necessary to apply a different simplified and adequate mathematical model, taking intoaccount conflicting criteria and objectives for uncertain disturbing factors, affecting on theoptimization system as a whole . In addition, satisfying all the designer/plannerrequirements, also satisfying both technical and economical constraints and social-technicalrequirements. In this paper, we derive the first objective function for the WOBAN planning anddesign which is to minimized the sum of the following items: installation cost for all ONUsrequired, plus installation cost for all WBSs required, plus cost of connecting WBSs to anONU and sum of cost WOBAN design. This function is defined[4, 6]: J cos. = f 1 (CONU j , CWBS.i , CONU j . , CWBSi . ; d ij ) → min inst . inst design design (1)The second objective is function of functions which is determine the optimal values of allitems that’s affecting in the proposed WOBAN architecture. This function is defined as thefollowing: J cov = f 2 ( PL, TR, PTx , PRx , IR, IP, RS , SD, f c ; NS , los / Nlos) → optimal (2)The parameters in function f j (•) after the “;” are define the uncertainties parameters whenthe parameters lie before that may be calculated and/or determined by the designer/planer anddepend on the technical features, specifications and configuration of the devices, where NSis the numbers of subscribers which is assumed unknown in this paper whereas Los / Nlos isline of sight and Non-line of sight in the geographical environment. The relation between all parameters and functions defined in (2) have a differenteffects and conflict situations, so we need to provide a better combination of the variousdimension of cost and effectiveness. The process of finding the cost-effective design isfurther complicated by uncertainty, which is shown in (1) and (2), so the projected cost andeffectiveness of a design are better described by a probability distribution. Distributionsresulting from designs and distributions associated with risky designs may have uncertaintywhich cause producing highly undesirable outcomes and presence of low-effectiveness/highcost. 124
  4. 4. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME We denote that in radio planning the channel modeling requires furthercharacterization on path loss propagation PL , fading and sometimes interference. PL is ameasure of average radio frequency (RF) attenuation endured by transmitted signal beforearriving at the receiver. For PL model, either empirical (statistical) model or site specificmodel can be used. An empirical model is simple to implement, requires less computationtime and is less sensitive to the environment while site-specific models are more accurate andvery complex to implement. The Stanford University Interim (SUI) models was developed by IEEE802.16working group are used in this paper, for details information about propagation models whichare used in planning channel modeling can referred to [12]. The basic path loss equation withcorrection factors is presented as: PL = A + 10γ log 10 (d / d o ) + L f + Lh + s (3)where, d is the distance between the access points and the user terminals’ antennas in meters,the d 0 =100m, A = 20. log 10 (4πd 0 / λ ) and γ = a − bhb + c / hb , here, hb is base station heightabove the ground in meters between 10m and 80m. To use this model for higher frequencies(upper 2GHz) and different receiver antennaheights, correction factors must be included[13].The correction factors for the operating frequency( L f ), is defined byL f = 6.0 log 10 ( f / 2000 ) and for the receiver antenna height ( Lh ) are given byLh = 10.6 log( hr / 2000 ) for type A and B while for type C the Lh is defined by Lh = −10.6 log( hr / 2000 ) and s is a log normally distributed factor that is used to account forthe shadowing fading to trees and other clutter and has a value between 8.2 dB and 10.6 dB. The grid separation distance(SD) is defined as the physical distance between any twocommunicating neighbors, is chosen suitably relative to the transmission range (TR). Assumethat, S is SINR threshold which satisfies the required BER, γ is the path loss exponent, P 0 Nis the background noise power, and P is the power received at a reference point in the far Rxfield region at a distance d from the transmitting antenna are given to we can compute the refTR by: TR = d ref ( PRx / S0 PN )1 / γThe grid separation distance equal to half the transmission range; i.e., SD = TR 2 . The interference range, IR is defined as the maximum distance at which the receivercorresponding to a reference transmission will be interfered with by another source (i.e., thereceived SINR at the reference receiver drops below the threshold S0 ), is given by[14]: IR = SD (1 /((1 / S0 ) − ( SD / d ref )γ ( PN / PRx )))1 / γ (4) To this end, we have to determine a link budget, LB . LB takes all of the gains andlosses of the transmitter through the medium to the receiver into account. Firstly, we need to calculate the maximum allowable path loss PLmax to which atransmitted signal can be subjected while still being detectable at the receiver. To determine PLmax we need to take the parameters into account. It is important to remark, that PLmax isdependent of the input power PTx of the antenna and thus dependent of the output power of thepower amplifier. Once we know the value of the PLmax , we can determine the maximum range TRmax ,so we can reach with the base station of a certain technology[15]. 125
  5. 5. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME TRmax = g −1 (PLmax − SM , f c , hBS , hMS ) (5)The quantity before the ”|” in (5) is a variable and varies over a continuous interval, while thequantities after the ”|” are parameters which take only one discrete known value. f c is carryfrequency (in Hz). The SM is the shadowing margin (in dB), hBS is the height of the basestation (in meters) and hMS is the height of the mobile station (in meters). The shadowingmargin depends on the standard deviation of the path loss model, the coverage percentageand the outdoor standard deviation. The generated interference in the network depends on the network topology used andthe transmission activity of the nodes in the network. This model is an energy-basedinterference model which takes into consideration radio related interference due to far awaytransmitting nodes. The interference of the wireless link could be assumed to be non-coherently combined at the receiver, and treat the interference of each node as white noise.The interference power, IP of all transmit nodes adds up to a total interference power labeledas IPtotal . The IPtotal of a random number of interferers N (a ) with random interference ψ (Rk )is given by[14]: N (a ) IPtotal = ∑ψ (Rk ) (6) k =1The PDF for the total interference power is only dependent on the transmit node density andgiven by: 3 −3 .λ2 / 4. IP ) PDF ( IP) = (π / 2)λt ⋅ IP 2 ⋅ e − (π t (7)Where λt is the transmit node density. The corresponding cumulative distributionfunction(CDF) is given by: CDF (IP ) = erfc(π 3 / 2 .λt / 2 IP ) Besides the above, there are factors which affect the signal received at the receiverdue to obstacles along the signal path for example Reflection and Refraction, Diffraction,Scattering and Multi-path interference. In addition, if the mobile receiver is moving, it is bestto include the effect of Doppler frequency shift model on the channel characterization. The receiver sensitivity, RS is defined as the minimum received signal power neededfor the receiver to achieve a given data and bit-error-rate, it is given by WiMAX forum [16]: RS = (kT / 2) ⋅ WLSR ⋅ NF ⋅ I implem. ⋅ SNRB …………….(8)This is further translated into: RS = 10. log((kT / 2) / 0.001) + 10. log(WLSR ) + L ……... (9) L + 10. log( NF ) + 10. log(I implem. ) + 10. log(SNRB )or RS = 177 + 10. log (WLSR ) + 10. log ( NF ) + 10. log (I implem. ) + 10. log(SNRB )WLSR is wireless link symbol rate, NF is define noise figure, I imolem. is degradation caused byimplementation limitations of noise ratio SNRB . SNRB is determine the theoretical basebandreceived signal power to noise ratio. NT is baseband value of thermal noise power and PRx isdefine the received signal power. The PRx / NT is needed to operate at the given bit-error-rate. For an alternative form of this expression, which uses Es / n0 instead of signal powerto noise ratio SNRB , where Es is the symbol energy and n0 is the single sided thermal noisespectral density. RS is then given by: 126
  6. 6. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME RS = −177 + 10. log(WLSR ) + 10. log( NF ) + 10. log( I implem. ) + 10. log(2 Es / n0 ) ….. (10)Finally LB with RS can be derived as: LB = EIRP − RS + GRx − SM totalWhere, GRx is the received antenna gain in dB and SM total is the total margin in dB includingshadow, interference, fading, etc. 3. PROBLEM FORMULATION The values of all criteria, objective functions and some parameters which to have beenneeded satisfying must be given or calculated by (1)-(10) and expressed as generalperformance index in parametric and criteria planes. This index show the efficiency of theHEA applied onto the WOBAN architecture.Thus, the task of optimal planning and optimal location of the WBSs and ONUs in order toachieve optimal solutions can be formulated as following mathematical mapping reflection: Φ( F ) : P × Q → V (11)In (11) is needed to find the optimal positions of WBSs and ONUs under the uncertainty,where r P ⊂ E r is set feasible values of position, Q ⊂ E q is set feasible values of uncertaintyparameters,V is set of vector – evaluation functions V (•) , V ( p, q, t ) ∈ E m is evaluation function at ( )moment t , t ∈ [t0 , T ] , and V p, q i ∈ E m is vector of performance index.By applying some mathematical transformations and computations we obtain the total errorwhich is needed to achieve: k m ( ) E ( p ) = ∑ ∑ ϕ T p, q i Λϕ j p, q i → min j ( ) p∈P (12) i =1 j =1 ( )where, Λ = diag λkk , k = 1, N + 1 - diagonal matrix of positions with size ( N × 1)( N × 1) . In this paper, we propose and investigate the game model formulation, which is apredeployment network optimization scheme, where the cost of WOBAN design isminimized (by placing reduced number of WBSs and ONUs, and planning an efficient fiberlayout). Also take in account the interference among multiple WBSs and ONUs, and otheraffecting factors, and explore several installation and assignment constraints that have to besatisfied for a better-quality access solution and maximized coverage. Our proposed game model for optimal WOBAN placement problem is formulated asthe multicriteria optimization problem under uncertainty as shown below[11] P , Q ,V ( p, q ) (13)Where: p ∈ P is vector of the WBS positions or ONUs locations. ˆq ∈ Q ⊂ Q is vector of uncertainty parameters.V ( p, q ) is vector of evaluation functions.We assume that, the sets P and Q given as system of nonlinear inequalities-constraints { P = p ∈ E r G1 ( p ) ≤ 0 s , 1 } (13a) Q ={ ∈E G2 (q ) ≤ 0s 2 }, rq q (13b)Where: 127
  7. 7. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME0s1 ∈ E s1 , 0s 2 ∈ E s 2 - vectors with non-zeros elements { = z ∈ E m Bz ≤ 0 p ,} (13c) - is dominant polyhedral cone defined by(13c).When we assume that, the matrix B with size [l × m] and z defined by z = v′′ − v′ , thenB (v ′′ − v ′ ) ≤ 0 p 4. THE RELATED WORKS AND PROPOSED HEA ALGORITHMSIn the recent few years the taxonomy of hybrid technology have big interest into specialistsand researchers, so that, especially the hybrid Metaheuristics have received considerableinterest in the field of combinatorial optimization problems [17] such as traveling tournamentproblem (TTP) [18], quadratic assignment problem (QAP) [9, 19], antenna placementproblem (APP) [20], node placement problem (NPP)[21, 22], integrated points placementproblem (IPP) [23] and transmitter location problem (TLP)[24] in different fields ofcommunication systems such as wireless sensor networks (WSN) [15, 21, 22, 24, 25],wireless local area networks (WLAN) [26, 27] design, and wireless ATM backbone networkdesign[28-30], femtocells optimization[31] and WOBAN[10] etc. The best results found formany practical or academic optimization problems are obtained by hybrid algorithms.Combination of algorithms such as descent local search, simulated annealing, Tabu search,integer programming, minimax algorithms, and evolutionary algorithms, and/or evolutionstrategies have provided very powerful search algorithms[18, 19,24]. Thus, they are not suited for the modeling of the our specific formulated problemwhich is considered in the previous section. In this case, the problem formulated in (13)-(13c) must be separated into two sub problems and each sub problem is solvedindependently. The first problem is WBSs placement problem for solving it, we propose thefirst hybrid evolutionary algorithm(HEA), denoted by HEA1, it is based on combination ofmulticriteria genetic algorithm(MGA) and hill climbing algorithm such as Topiks-Veinottalgorithm (TVA)[11]. Our primary goal is to place multiple WBSs (say N of them) properlyin the selected geographical area. Assume that Pi ( xi , yi ) is the position of i-th WBSs, whichwill serve users and Pj ( x j , y j ) is the position of j-th ONUs which will serve multiple ofWBSs. The second problem is ONUs placement problem for solving it, we propose thesecond hybrid evolutionary algorithm(HEA), denoted by HEA2, it is based on combination ofmulticriteria genetic algorithm and hill climbing algorithm such as modified Tornqvistalgorithm(MTA)[32, 33]. Here, our goal is to place multiple ONUs (say M of them) properlyin a geographical service area, where the user’s locations are known beforehand from laststage.The proposed HEA1 has two main phases, a global search phase based on multicriteriagenetic algorithm and a local search phase based on modified Topiks-Veinott algorithm. Thegoal of the global search phase is to cover the search space as broadly as possible in order toidentify a good start point for the local search phase initialization. The local search phase thenstarts from the starting point which is selected in the global search and applies a gradient-based method or heuristic search algorithm such as hill climbing algorithm to search aroundits neighborhood for finding a better solution or near-optimal solution from optimal feasiblesolutions. 128
  8. 8. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEMEFor both global search and local search phases, we modify both algorithms by applying anovel neighborhood search technique based on constructing locally a cone dominate spacefunction from each founded point in each iteration, which leads to better convergence foroverall hybrid evolutionary algorithm.4.1 The first hybrid evolutionary genetic algorithm (HEA1) for multiple WBSsplacement problem 4.1.1 Global search phase: MGAIn this phase is applied the multicriteria genetic algorithm consist of the following blocks:The first block is randomly generate of the first generation of population and encoded in bitstring such as : { A(t ) = a i (t ), i = 1, N ; dim a i (t ) = L } (14)Where: N is population size; L is bit string length; a (t ) is bit string length; t is number of i [ ]current generation , t ∈ 1, T and T is the last generation.In each of a (t ) is encoded all information about all vector elements pi (t ) . iThe second block consists of the following two steps:Step 1 : The population decoding is obtained by: ∆ : a i (t ) → pi (t ) , i = 1, NEach partial lµ , µ = 1, n , bit sting ai (t ) is represented into nature number Cµ (t ) according to i ithe following: M 0, aµk (t ) = 0  i C µ (t ) = ∑ Cµk (t ), and Cµk (t ) =  i i i (15) k =1 (− 1) 2  p +1 ( M − k +1 ) − 1 , aµk (t ) = 1 iThe bit string aµ (t ) of the element pµ (t ) of the vector pi (t ) is defined by Grey code. i iThe coordinate values of the vector p i (t ), i = 1, N is calculated by: pµ (t ) = pLµ + (C µ (t )( pHµ − pLµ ) / 2 µ −1 ), µ = 1, r i iStep 2 : Fitness function calculation. ( )Compute the V p i (t ) , i = 1, N . In each of individual p i (t ), i = 1, N we must to apply the ε − Ω − optimal principal condition which is formulated in following such as: [(( ( )) ) ( )] B V p i (t ) + Cε p j (t ) − p i (t ) − V p i (t ) ≤ 0 p (16)For ∀p (t ), j = 1, N , j ≠ i jThe number of points p j (t ) is denoted as bi (t ) , for which it’s in the point pi (t ) is executedthe inequalities constraints (16).If bi (t ) = 0 then pi (t ) is ε − Ω − optimal solution for the multicriteria optimization problemwhich is formulated in (13 -13c), so that, pi (t ) is ε − Ω − optimal solution for multicriteriaoptimization problem which is formulated as: min V ( p ) (17) p∈POtherwise, when pi (t ) is not ε − Ω − optimal solution for problem (17), to 0 ≤ bi (t )≤N − 1Thus, the fitness function in this case is formulated such as: Φ (a i (t )) = 1 /(1 + (bi (t ) /( N − 1))) q ≥ γ ∗ (18) 129
  9. 9. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEMEWhere q is selected from practical and effect on convergence speed of proposed algorithm. 1 ( ) ( )In (18) shown that when bi (t ) = 0 to Φ ai (t ) = 1 , and when bi (t ) = N − 1 to Φ a i (t ) = q . 2The third block is forming the probability selection mechanism of the population in parentsset Θ(t ) . This mechanism based on the best selection of population with exception properties.This is mechanism is presented by the following steps:Step (1): The interval [0, S (t )] is constructed by following recurrent relations: S 1 (t ) = Φ (a 1 (t )), L S N (t ) = S N −1 (t ) + Φ (a N (t ))We assume that S (t ) = S N (t ) .Step (2): Generate the points-parents populationFirst, the random value is generated on the interval [0, S (t )] . The population is selected in theparents set, if the random value lies on the subinterval from [0, S (t )] . After that, the suitablesubinterval was excepted from the interval [0, S (t )] . This is procedure is repeated N / 2 once.The crossover and mutation operations with analogy the selection mechanism operationswhich is above presented. The probability of mutation is selected on the interval [0, Pm ] . Inthis work we apply two types of mutation operations.The forth block is the stop criteria which is presented such as: 1. Check the following condition (n(t ) / N ) ≥ δ ∗ (19)Where n(t ) is define the number of individual in the population A(t ) with size N. For that’s itthe inequality: Φ(a i (t )) ≥ γ ∗ must be executed.If the(19) is executed, to the set of the points p A (t ) ⊂ P is approximation of the ε − Ω −optimal solution. That is mean that PεΩ = p A (t ) and multicriteria genetic algorithm is ˆfinished. Otherwise, if t ≥ t ∗ , the set pA (t ) is approximation of the ε − Ω − optimal solutionand multicriteria genetic algorithm is finished. 2. Initial points selection:This block is needed for initial setting multicriteria local algorithm which is consist of thefollowing steps:Step1: The vector performance index V ( p ) is normalized with the following representationoperation: ~ V ( p ) − VLi Vi ( p ) = i , i = 1, m VHi − VLiWhere VHi , VLi are maximum and minimum of possible values of the Vi ( p ) VHi = max Vi ( p ) , p∈ pεΩ ˆ VLi = max Vi ( p ) , i = 1, m p∈ pεΩ ˆ 0ˆStep2: The initial approximation p ∈ PεΩ is constructed by applying the following: ~ Φ (V ( p )) = max Vi ( p ) i∈M 0 ˆFind the p ∈ PεΩ by solving the optimization problem: 130
  10. 10. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME min Φ (V ( P )) ⇒ p0 ˆ (20) p∈PεΩ4.1.2 The local search phase: TVAIn this phase is applied the multicriteria local algorithm based on hill- climbing algorithmsuch as TVA. The TVA is that, firstly, the optimal solutions are sought along of gradientdirection(algorithm1). If the optimal solutions not found along of gradient direction, then thedirection is changed in to 180, that’s means, the optimal solutions must be sought in thedirection of anti-gradient(algorithm 2). The cycle is repeated until there are the optimalsolutions not found. In this paper, the two cases are considered. In the first case, the searchingof the next better solution is taken along of gradient direction (algorithm 1), while in thesecond case the searching of the next better solution is taken anti gradient direction (thesteepest descent algorithm is algorithm 2).The algorithm1: The search along of gradient direction. This algorithm belongs to theiterative gradient-based optimization methods and depends on the following rule p ( k +1 ) = p ( k ) + α ( k ) d ( k ) ,For finding the next better point using above iterative rule, we construct the polyhedraldominance cone from approximated pareto set founded in the last phase by MGA and selectfeasible directions d ∈ P ( p ) ⊂ E r inside the constructed dominance cone. This algorithmconsist of following steps:Step1: The selection of d ∈ P ( p ) ⊂ E r when P ( p ) like the type of (13c) which isconstructed in the space of the locations pi of the WBSs at the current point p . Using thedominance condition as Hill-down or Hill-up condition, we can be to formulate the problemof the Hill-down or Hill-up condition direction at the point p ∈ P inside the dominance conesuch as: max z (21) [d , z ]∈D TWhere, the D is given as inequalities-constraints:  ∂V ( p )  B ∂p d + Z p ≤ 0 p , (21a )   ∂G ( p ) D : B 1 d + Z S1 ≤ −G1 ( p ), (21b )  ∂p  d ≤1 (21c )  k  p s1Where Z p ∈ E , Z s1 ∈ E is the vector with the same element z and B is quadratic matrix ofthe polyhedral dominance cone.The equation (21a) is denoted by d ∈ Ω P ( p ) and its means Hill-down direction condition atinside the cone Ω .The equation (21b) is the feasible direction d which is taking in account both active and non-active inequalities-constraints at the point P .The equation (21c) is the inequality is vector norm condition of d k-th order.Stop condition of the feasible direction selection algorithm is formulated by the following: & & V ( p ∗ )T B T ⋅ µ + G1a ( p ∗ ) ⋅ν = 0 (22) 131
  11. 11. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME ∗ ( ) sWhere G1 a p ∈ E 1 a - constraints vector which is (22) active at point P*. The followingcondition z ≤ γ is stop criteria of the multicriteria local optimization. Where γ is show theaccuracy of the multicriteria local search.Step2: step size α (k ) calculation in the selected direction d (k )1: firstly, finding the distance to the boundary of the feasible area P is calculated along ofthe direction d (k ) { P = p ∈ E r pL ≤ p ≤ pH }For that, P may be represented by another form: P = p ∈ E r Cp ≤ b { }Assume that, cT = c1 , c2 T T [ ] [T T ] ; bT = b1 , b2 , so that c1 p (k ) = b1 and c2 p( k ) ≤ b2 (k ) (k )The distance λd to feasible area boundaries at the point p is defined by:  ˆ b (k )   min  j ˆ (k ) d (jk ) ≥ 0  (k )  ˆ λd =   dj  ,   ˆ (k ) ∞ , if all d j ≤ 0  ˆ (k )Where d = c2 d (k ) ; b ( k ) = b2 − c2 p (k ) ˆ (k )2: step size selection α is calculated by following operators dented by OP1-OP4:OP1: α = min{ 0 , λd }, (k ) (k ) α ( k + 1)OP2: p = p ( k ) + α ( k )d ( k ) , (k )OP3: ∆ν = ν p ( ( k +1 ) ) ( ) −ν p ( k ) , (k )OP4: ∆ν ∈ Ω ⇔ B∆ν (k ) ≤ 0 p (k )If the OP4 in not excepted to, we increment the α and step by step repeat the operators(OP2-OP4).The algorithm 2: The search of the next better point is taken anti gradient direction(here,used steepest descent algorithm). This algorithm is similarly to the algorithm 1, but theiterative rule is changed into p( k +1) = p (k ) − α ( k )d (k ) , and the min ~ z instead of (21) and the T [d , z ]∈D(21a-21c) can be changeable and the stop criteria condition (22) is reformulated in anotherform.4.2 The Second hybrid evolutionary algorithm(HEA2) for multiple ONUs placement problemThe HEA2 proposed for solving the multiple ONUs placement problem based on combination ofMGA and MTA. The HEA2 too has two phases. The first phase is the MGA which is consideredbefore, whereas, the MTA is applied in the second phase.The modified Tornqvist algorithm(MTA)The second local phase search algorithm is the modified Tornqvist algorithm adapted to our planarlocation model proposed in this paper. The Tornqvist’s algorithm was first defined 25 year ago. It isdeterministic algorithm belonging to a family of local hill-climbing search methods. It has beenproven to perform very well on simple planar location or planar covering our task. In addition, it canbe easily adapted to include not enough information about feasible locations of ONUs, propagationenvironment, number of subscribers and service areas etc., which causes uncertainties situation aswell as presented in the problem statement and formulation. The method involved a series of movesover the search space; where each move attempted to improve the objective function. When no furthermove can be found that would result in an improvement or, in instances in which an identifiedimprovement falls below a pre-determined critical value, the algorithm terminates. The hill-climbing 132
  12. 12. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEMEalgorithm could be defined as f jk+ 1 ( Pj +1 ) = f jk ( Pj ) + s k d =θ v , { } θ v = 0, 2π , where k +1 k k +1f j +1 ( Pj +1 ) > f ( Pj ) , and f j j +1 ( Pj +1 ) is the values of the objective functions at the next locations. kf ( Pj ) is the values of the objective function in the previous locations, and s k is a step vector and θ v jis the angles of direction, so that, the steps may be taken dynamically adjusted. In this paper, the MTA is initialized by initial points set of positions obtained by MGA. Thenthe algorithm executes a series of moves (steps) according to a search plan (hence, its designation as a`deterministic algorithm) in the selected directions until no more improvements can be made. TheMTA includes the neighborhood search method based on dominance cone construction (13c) fromeach one of the locations in all steps along of all directions. The general flowchart of the modified TAcan be presented in the following fig.2: Initial set of ONUs position (obtained before hand by MGA P 0 ) Position selection mechanism t =0 →t =T k k Do move ( x j , y j , s , d ) Step change s ← s + 1 Dominance cone construction f j+1 (P ) = f j(P ) + Fitness function calculations Evaluation function calculation V k +1 ( P j +1 ) = V k ( Pj ) + s k d =θ k k k +1 k ∆V = V ( P j +1 ) − V ( P j ) ∆V k ∈ ⇔ B ⋅ z <0 p Direction change d ← d +1 k No ∆V > 0p Yes stop criteria print the best optimal of the ONUs positions Fig.2 The general flowchart of MTA 133
  13. 13. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEMEWhere, in the fig.2. move ( x j , y j , s k , d k d =θ v ) expresses the search from a point p j ( x j , y j ) ,with a step s in a direction d , min(s ) is a minimum step, ∆f k ( p j ) is a change in an objectivefunction values, change direction ∆d d =θ v is a change in the direction of a search, change step∆s s ← s +1 is a change in the search step, and T is the maximum number of iterations allowed. 5. PERFORMANCE STUDYThe performance of proposed HEA was evaluated through application of the HEA1 to thetwo data sets for WBS locations of Yemen mobile company in Hodeidah City and HodeidahGovernment which are distributed and represented in the Geographical maps as shown as inthe (fig.3.a) and (fig.3.b). Fig3.a. Geographical map of Hodeidah city. fig.3.b. Geographical map of Hodeidah governmentOur experiments for performance investigation of the proposed HEA1 are carried out in twocases. In the first case, we will use the data sets of WBSs locations for Hodeidah City andHodeidah Government installed by Yemen Mobile company. The HEA1 is applied on thegiven data sets for finding the optimal locations of WBSs.The results of the modeling show that the proposed HEA1 finds the optimal locations ofWBSs at the given parameters for Hodeidah City as illustrated in the fig.4. Fig.4 Optimal locations of WBs by HEA1 for Fig.5 Optimal locations of WiMAX WBs and ONUs by Hodeida city HEA2 for Hodeida Government 134
  14. 14. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEMEut in the second case, we assume that Yemen Mobile Company want to develop the itsnetwork by applying the new 4G technology by using hybrid WOBAN architecture (whichabove introduced) and WiMAX WBSs and ONUs installation for increasing of the overallnetwork performance. For this reason, we will use the data sets locations of WBSs applied inthe first case as potential locations for WiMAX WBSs and ONUs placement, we use themethodology presented in [34-36]. For this purpose, the geographical map is separated intothree regions and represented as three quadrates/clusters, where each quadrate/cluster definesthe service area and consists of permissible locations which belong to the feasible locationset. Assume that, each of region, so called cluster with a unknown numbers of WiMAXWBSs and each one cluster has one a ONU to be providing the maximum connectionbetween the ONU and other multiple WiMAX WBSs. The network of WiMAX WBSs can besimulated with a hexagonal model which simplifies calculations of some operationalparameters of the wireless system such as channel interference, power interference, path loss,interference range and receiver sensitivity etc considered in the section (2).The quality of the wireless communication network of WOBAN architecture depends,largely, on the locations of the WiMAX WBSs and ONUs. The WiMAX WBSs located infavorable locations will assure desirable signal quality and desirable maximum coverage, so agood quality of service. Conversely, poorly located of WiMAX WBSs will create inadequatesignal coverage, degrading overall network performance. Our proposed algorithm dented byHEA1 for solving the WiMAX WBSs placement problem takes into account theenvironmental factors, economical and technical aspects. In addition, it is takes into accountthe uncertainty parameters. After WiMAX WBSs positioning and deployment should beONUs placement and deployment, for this purpose, the HEA2 is applied for finding theoptimal locations of ONUs. The fig.5 illustrate the optimal locations of WiMAX WBSs andONUs. J cov V( p, q,t) 1 1 0.9 0.9 Hill climbing. P erform ance index (% ) 0.8 Coverage normalized 0.8 MGA 0.7 0.7 HEA (MGA+ Hill climbing) 0.6 0.6 HEA best sol. HEA opt. sol. 0.5 0.5 0.4 0.4 HEA worst sol. 0.3 0.3 0.2 0.2 0.1 0.1 0 0 0 20 40 60 80 100 120 140 160 180 200 220 0 20 40 60 80 100 120 140 160 180 200 220 Generations, t Generations, t Fig.7 Performance comparison of HEA(MGA+ Hill climbing) Fig.6 Convergence of HEA With MGA and Hill climbing independentlyThe results of our experiments on the selected data set depict the characteristic feature ofproposed HEA1 and HEA2 a relatively small number of generation are necessary for thealgorithm convergence (whereas traditional evolutionary algorithms or local searchalgorithms independently may require hundred of generations/iterations) for converge (fig.6).In addition, our proposed algorithms do not trapping in the local optimum because of theHEA1 and HEA2 includes the neighborhood search method based on dominance coneconstruction (13c) from each one of the locations in all steps along of all directions. Fig7.Illustrates performance comparison of HEA with MGA and Hill climbing, independently. 135
  15. 15. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME 6. CONCLUSIONWe investigated two problems of multiple WBSs/WiMAX WBSs and multiple ONUsoptimal placement in the hybrid WOBAN architecture. The HEA1 is applied for solving,independently, the WBSs/WiMAX WBSs placement problem and the HEA2 is applied forsolving the multiple ONUs placement problem. The results obtained by using the hybridevolutionary algorithms performs very well optimization techniques for solving themulticriteria optimization problems which formulated in this paper. With comparison withtraditional optimization methods the proposed HEA achieve a result close to the globaloptimum and require at minimum time consuming. In addition, they reach high accuracy andsatisfy all designer/planner and economic-technical requirements taking in to account theparameters of uncertainty defined in problem formulation. 7. ACKNOWLEDGEMENTThe authors thank the Yemen Mobile Company for supporting this project, especially, manythanks to the engineer Sami Kafla for providing the data sets and a great tanks to ProfessorHussein Omar Qadi, President of Hodeidah University for his supporting to develop theacademic research and researchers in the Hodeidah University. 8. REFERENCES[1] Aldajani M. A.(2008), “Convolution-based placement of wireless base stations in urban environment”, IEEE Transactions on Vehicular Technology,57 (6):3843-3848.[2] Sarkar S.(2008), “Design and analysis of Wireless-Optical Broadband Access Networks (WOBAN)”, Dissertation of PhD. University of California, Davis, p.120.[3] Sarkar S., Mukherjee B., and Dixit S.(2006), “Optimum placement of multiple optical network units (ONUs) in Optical-Wireless Hybrid Access Networks”, Proceeding of IEEE/OSA Optical Fiber Communications (OFC), Anaheim, California, March 2006.[4] Sarkar S., Mukherjee B., and Dixit S.(2006), “Towards Global Optimization of Multiple ONUs Placement in Hybrid Optical-Wireless Broadband Access Networks”, Proceeding of IEEE Conference on Optical Internet (COIN), Jeju, South Korea, July 2006.[5] Wright M.(1998), “Optimization methods for base station placement in wireless applications”, Proceeding of IEEE Vehicular Technology Conference (VTC), Ottawa, Canada, May 1998.[6] Molina A., Athanasiadou G., and Nix A.(1999), “The Automatic location of base-stations for optimized cellular coverage: A new combinatorial approach”, Proceeding of IEEE Vehicular Technology Conference (VTC), Amsterdam, Netherlands, September 1999.[7] Nagy L. and Farkas L.(2000), “Indoor base station location optimization using genetic algorithms”, Proc., Personal, Indoor and Mobile Radio Communications, London, UK, September 2000.[8] Pulak K Chowdhury. “Bottleneck analysis in WOBAN: Wireless Optical hybrid Broadband Access Networks”, Dept. of Computer Science, University of California, Davis, CA.[9] Sarkar. S.; Dixit, S.; and Mukherjee, B.(2009), “Hybrid Wireless-Optical Broadband- Access Network (WOBAN): A Review of Relevant Challenges”.[10] Henk Wymeersch, Jaime Lien, and Moe Z. Win.(2009), “Cooperative localization in Wireless Networks”. Proceedings of the IEEE, February 2009; 97(2):427-450. 136
  16. 16. International Journal of Electronics and Communication Engineering & Technology (IJECET), ISSN0976 – 6464(Print), ISSN 0976 – 6472(Online) Volume 3, Issue 3, October- December (2012), © IAEME[11] Holba, Yousef Yahyia.(2002), “Composite procedure of multiobjective optimization algorithms parameters of neural-control dynamic systems under uncertainty”, Dissertation of Ph.D(In Russian lang.), M., Russian National Public Library, 2002.[12] Hrovat Andrej, Javornik Tomaz, Plevel Sreco, Novak Roman, Celecer Tine, Ozimek Igor.(2006), “Comparison of WiMAX field measurements and empirical path loss model in urban and suburban environment”, Proceedings of the 10th WSEAS International Conference on Communications, Vouliagmeni, Athens, Greece, July 10-12, 2006; pp. 374-378.[13] Javornik Tomaz, Kandus Gorazd, Hrovat Andrej, and Ozimek Igor.(2006), “Comparison of WiMAX coverage at 450MHz and 3.5GHz”, 7th COST 290 Management Committee and Technical Meeting Split, September 29-30, 2006.[14] Deruyck Margot, Tanghe Emmeric, Joseph Wout, Vereecken Willem, Pickavet Mario, Dhoedt Bart, Martens and Luc.(2010), “Towards a deployment tool for wireless access networks with minimal power consumption”, IEEE 21st International Symposium on Personal, Indoor and Mobile Radio Communications Workshops (PIMRC Workshops), pp. 294 – 299.[15] Konstantinos P. Ferentinos, and Theodore A. Tsiligiridis.(2007), “Adaptive design optimization of wireless sensor networks using genetic algorithms”, Elsevier Science, Science Direct, Computer Networks, 51:1031–1051.[16]. WiMAX Forum,[17] E.-G. Talbi.(2022), “A taxonomy of hybrid Metaheuristics”. Journal of Heuristics, Kluwer Academic Publishers, Vol. 8, pp.541–564.[18] A. Lim , B. Rodrigues, and X. Zhang.(2005), “A simulated annealing and hill-climbing algorithm for the traveling tournament problem”, European Journal of Operational Research Elsevier, Vol. xxxx, (accepted), pp. xxx–xxx.[19] Volker Niessen.(1994), “Solving a quadratic assignment problem with clues from nature”, IEEE transaction on neural networks, January 1994; 5(1):66-72.[20] Larry Raisanen and Roger M. Whitaker.(2005), “Comparison and evaluation of multiple objective genetic algorithms for the antenna placement problem”, Springer Science, 10(1- 2):79-88.[21] Rabindra ku Jena.(2010), “Multiobjective node placement methodology for Wireless Sensor Network”, IJCA Special Issue on “Mobile Ad-hoc Networks” MANETs, 2010.[22] Amol P. Bhondekar, Renu Vig, Madan Lal Singla, C Ghanshyam, and Pawan Kapur.(2009), “Genetic algorithm based node placement methodology for Wireless Sensor Networks”. Proceedings of the International Multi Conference of Engineers and Computer Sciences 2009 Vol. I IMECS 2009, March 18 - 20, 2009, Hong Kong.[23] Yu Liu, Chi Zhou, Yu Cheng.(2011), “S2 U: An efficient algorithm for optimal integrated points placement in hybrid optical-wireless access networks”, Elsevier. Computer Communications, Vol. 34:1375–1388.[24] Krzanowski R.M. , Raper J.(1999), “Hybrid genetic algorithm for transmitter location in wireless networks”, Elsevier Science, Computers, Environment and Urban Systems Vol. 23, pp. 359-382.[25] Amitangshu Pal(2010), “Localization algorithms in Wireless Sensor Network: Current approaches and future challenges”, Journal of Network protocols and Algorithms. 2010; Vol. 2 No1, pp.45-74.[26] Alan Mc Gibney, Martin Klepal, and Dirk Pesch.(2011), “Agent-based optimization for large scale WLAN design”, IEEE Transactions on evolutionary computation, 15(4), August 2011. 137
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