Study On Lion Swarm Optimization Algorithm In Wireless .

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High Technology LettersISSN NO : 1006-6748Study On Lion Swarm Optimization Algorithm In Wireless Sensor NetworkAla Sabree AwadCollege of Computer Science and IT, University of Anbar, Ramadi, IraqAhmed N. RashidCollege of Computer Science and IT, University of Anbar, Ramadi, IraqDr. Khalid Shaker alheetyCollege of Computer Science and IT, University of Anbar, Ramadi, IraqABSTRACT - In most of the real world applications the broad range of Wireless Sensor Networks (WSN) is fruitfullyadopted. Several WSN safety measures have been exploited in security purposes such as protecting password. Numerousadvancements have been exhibited in WSN with the aid of optimization approaches. Economic landscape of the wirelesssensors makes it very much predominant in all applications. Maintaining accurate energy efficiency is the principal concernof WSN. Even though achieving the optimized energy efficiency stills remains a challenge, the existing researches boonsfrequent validations to initiate energy efficiency. The optimization based clustering protocols are in advance, supremelyideal in wireless sensor networks for achieving energy efficient means of routing procedure. In this current research, severalwide applications of Lion swarm optimization algorithm in wireless sensor network are discussed in detail.KEYWORDS--- energy efficiency, Lion swarm optimization, Wireless Sensor Networks.1.INTRODUCTIONWide range of assembly of interconnected sensors surrounded by the wireless medium is encompassed in a WirelessSensor Network (WSN). The base station endeavors as the prime access point for the series of the sensor devices inthe whole network in WSN model. Weather monitoring, meteorological data collection and field surveillance are theseveral fields in which the broad extend of WSN are applicable.More than a few kind of developments andinnovations of WSN has produced positive impacts in military, science, commerce and health-care fields.The quality of the sensors existing in the wireless networks decides the efficiency of the network performance. Theideal factors necessary for any network is better precision, enriched accuracy and robustness to noise [2]. A newlysuggested stochastic global search process is known as Ant Lion Optimizer (ALO). Presently in several researchworks, ACO has been widely used such as for simulation optimization for the controller. In case of facing randomload pattern as disturbance ACO helps in attaining better results. Dynamic and non-convex load dispatch issues thatexist in an electric power system can be rectified with the aid of ALO approach. For the purpose of avoiding localoptima ALO is capable of upholding an adequate equilibrium position between the exploration and the exploitationphase [3]. The principle of naturel division of labor with the existing population size is the process of LSO by followingthe competition law, “Natural selection and survival of the fittest”. The cubs are ought to learn how to hunt prey andeat. These cubs might be excluded out only if they do not pretend to become the king when they turn out to be adults.Since the discrepancies of recent mechanisms are found to endorse fast convergence rate, the LSO do not fall intolocal optima easily [11]. The main process of lion optimization approach is mentioned as:Volume 26, Issue 12, 2020451http://www.gjstx-e.cn/

High Technology LettersISSN NO : 1006-6748StartDefine the specific parameters of the algorithmThe position of ants and ant-lion are initiatedFitness function of ant sand ant-lions are calculatedIter 1Determine ant-lionSelect ant-lions for each antThe position of the any is updated by using random walk with respect to selected ant-lionCalculate ant’s fitness function and update ant-lion’s current positionIter Iter 1YesIt ITNoFinalize the best ant-lion’s positionEndFigure 1. Ant-Lion Optimization Flowchart representationVolume 26, Issue 12, 2020452http://www.gjstx-e.cn/

High Technology LettersISSN NO : 1006-6748LSO algorithm explores for the most relevant optimized values for the objective function [32]. Maziar Yazdani in2016 introduced nature-inspired meta-heuristic Lion Optimization Algorithm (LOA). Usually lions hunt all togetherwith other fellows of their pride. In and around the specific solution space LOA procures an actual thought-outapproach for clasping the prey and catching it from any extensively produced inhabitants. By constant cooperation ofall lionesses they surround the prey from alternate positions and opt to catch the prey in an active manner [33].A novel congestion management method was proposed in the origin of rearranging stratagem. This particular approachaimed in minimizing the congestion with condensed cost of rescheduling. The ultimate supremacy of the advancedsystem is examined and evaluated particularly by means of cost analysis. In this manner the recommendedrescheduling based congestion management model Improved Lion Algorithm outperforms the other probable modelsby reducing the congestion with least rescheduling rate [34].For amplifying the combinatorial NP hard elastic process scheduling problem an advanced approach based on ALOis presented. For the persistence of labeling the process flexibility, sequential flexibility and machine flexibility thenetwork demo practice is well suitable [35]. In the family of nature inspired algorithms lion algorithm is introducedby adopting the inspiration of exclusive social activities of lion that makes the animal much stronger than othermammals.On the base of lion algorithm an extensive study is directed in which four diverse test sets has been accomplished.Among them the first two test suites are benchmark optimization problems with uni-modal and multimodalcharacteristic features [36]. This approach recommended an innovative nature inspired algorithm known as ALO. Themajor motivation for the development of this methodology is the hunting concert of ant-lions and entrapment of antsin ant-lions.Quite a few operators were anticipated and scientifically exhibited for furnishing the ALO algorithm with highqualitative exploration and exploitation [37]. This research projected the multiple objective limits essential forresolving a vehicle routing problem for VANET. For achieving this, a vehicle routing problem prototypical model hadbeen recommended that primarily depends on the collision, congestion, travel, and QoS cost. The QoS based costfunction had been derived by means of the fuzzy inference system. LA has been subjugated for unraveling the routingmodel. The computational time period is calculated along with the cost and convergence [39].For enhancing the community based detection problems in networks Ant Lion Optimization is used commendably.For the purpose of seizing the insights of communities the ALO algorithm is applied with two diverse qualityfunctions. In terms of accuracy the gained results exposed that the performance of the projected algorithm is positivelyfavorable and it grandly determines an outstanding community arrangement.More impending research inspects the setting standards for intensifying the precision and scalability of communitydetection concern [45]. This research work offered Lion Optimization Algorithm for the community detection innetworks problem. Future work focuses on diverse nature inspired methods to identify communities in networks [46].In IEEE 118 bus systems the proposed method is performed [48].Calibrating DBN factors with Ant Lion Optimizer algorithm approach is described in this research. The correspondingresearch experiments were executed with the aid of binary image reconstruction using DBNs of different layers.Moreover DBNPSO was considered and displayed that they achieved effective outcomes for all existing layers. Byintegrating ALO awfully better results could be attained with more layers being measured. The expense of using moreiteration exists for convergence. In future research works DBNALO interactions are taken in account to review theprogress in the experiments conducted. This research work means to employ innovative optimization schemes of finetune DBMs [49].Volume 26, Issue 12, 2020453http://www.gjstx-e.cn/

High Technology LettersISSN NO : 1006-6748For the purpose of proper locating and perfect sizing of DGs in DS a widespread optimization method is been accepted.The ALO has promised the outstanding results from the insight of objective function value [51]. The numerical resultsrecognized that the ALO algorithm is capable of producing bigger solutions with worthy performance of convergence[52]. An effective search system is described once after the hunting action of lion algorithm. Here the hydro-thermalwind concern is unraveled.In order to aid the fitness standard for the MO problem an overall ranking index, (CRI) is been put forward. Thepractical constraints of hydro, thermal and wind generation is well maintained by concerning ALO and CRI andproductive Pareto-optical results are achieved as an outcome. The costing is of 6.75% once after the wind integrationbecause of WP insecurity cost. Some of the greenhouse gas effects such as NOx, SOx and COx were compressed ina range of 51.1%, 21.5% and 56% self-reliantly [53].In case of non-linear optimization the suggested e-ALO might institute itself as an expectant entrant. This kind ofindispensible outset has engaged to the formation of the new system as accessible at this point. In order to quantifythe significance of several non-uniform distributions like Burr, Pareto, Cauchy and Beta ALO is very much pragmatic[55]. The diversity in population is managed due to the systematic assessment of the exploration space in ALO overthe random walks that are being created for each ant dimension aspect [56]. Ant-lions in high-density circumstancesalso deal with sand thrown by their neighbors and are susceptible to cannibalism [59].2.RELATED WORKSAnt Lion Optimization (ALO) algorithm was acquainted to the antenna and electromagnetics community disquiet [5].A dexterous encryption process that satisfies the HE to implement encryption and decryption on every one of theimages is suggested. With the support of Ant Lion Optimization (ALO) algorithm the decryption system is exploited[12].The presentation of the LSO algorithm could be amended over proper countless approaches. Nevertheless, the purposeof this research is to equate the performance and achievability of the typical version of the LSO algorithm with thoseof other state-of-the-art meta-heuristic optimization processes [13].In radial distribution networks a novel implementation of ALOA and LSF index has been discoursed. Renewable DGhas been deliberated as an alternative of carbon intensive energy bases to reduce the global warming radiations. Themajor assistances of this research work include design of multi-objective function to diminish the total power lossesand develop the voltage profiles and VSI of distribution systems. For attaining this ALOA is suggested to optimallyperceive the site and size of renewable DG [15]. LOA is described based on unsociable simulation model reliablemovement [17].For the purpose of optimal location and sizing of DG centered renewable sources in immeasurable distribution systemsALOA has been admirably activated. The mentioned issue has been described as an optimization task which includescalculating power losses, voltage profiles and VSI. The efficiency of the provided approach is described by means ofseveral test systems and the results have been compared with respect to other existing methods [18].This research work intends a novel algorithm that groups both LSO and GA to amplify the unique LSSVM model forcarbon dioxide emissions. This algorithm might expound the impulsive convergence issue of GA and it could achievethe most global optimal solution. By implementing this algorithm and assessment of carbon dioxide emissionanticipating effects, appropriate conclusions were made. It includes, by connecting with the other eight algorithms,LSO-GA algorithm has stronger global optimization capacity as the optimal solution it secures has the lowest variancefrom the particular ideal result. Moreover, it also has a quicker convergence speed [19]. This research study offers aunique swarm based optimization algorithm which mimics the sea lions' hunting behavior. The promoted method isVolume 26, Issue 12, 2020454http://www.gjstx-e.cn/

High Technology LettersISSN NO : 1006-6748known as Sea Lion Optimization (SLnO) algorithm. This tangled three main features to put on the examination of baitball using the whiskers of sea lions, enclosing bait ball and the vocalization of sea lions [20].Sub-populations execute the DLSO algorithm in parallel over the Message Passing Interface (MPI), and it is coupledby ring topological association. Sub-populations transfer the optimal individuals amongst areas [21]. A modern metaheuristic system explicitly ant lion optimizer for unraveling ORPD problem has been presented in this research. Theperformance metrics of ALO was estimated by means of IEEE 30- bus system. The simulation outcomes showed thatALO able to attain minimum loss related to other methods anticipated in the past works [24].An innovative meta-heuristic algorithm has been established. Using the policy of group hunters in grasping their preythe Hunting Search (HuS) meta-heuristic optimization algorithm was abstracted. The HuS algorithm enforces lessmathematical provisions and does not include initial value settings of the decision variables connected to gradientbased mathematical optimization schemes. Additionally the HuS algorithm uses stochastic searches and so, derivativeevidence is avoidable. Certain benchmark optimization difficulties were enlightened to certify the efficacy andtoughness of the new algorithm while linked to other optimization approaches [25].Lion’s optimization algorithm was suggested and experimentally compared with other existing common evolutionaryprogramming systems [26].Basic representation of lion pride optimization algorithm is as follows:WingerCheaterDirection of preychangesCircles the prey andattacks from all sidesPreyTrying to catch preyDirection of cheater’smovementCheaterFigure 2. Lion pride optimization algorithmThe MLP training problems were framed for the ALO algorithm for determining the optimal values for the weightsand biases [28].The economic load dispatch concerns in electric power system are resolved by using ALO algorithm. In small scalepower plants the act of the ALO algorithm is certified and the success rate of proposed ALO algorithm is establishedin standard IEEE bus system entailing of 3, 5 and 6 generating units model by considering transmission losses andvalve point results [29].Volume 26, Issue 12, 2020455http://www.gjstx-e.cn/

High Technology LettersISSN NO : 1006-6748The research offered in this work inspects the applicability of MOALO algorithm in enlightening the large-scaleMOORPD problem in an exertion to progress the performance of power systems operation and reduce active powerlosses by specific tuning of control variables. The ORPD problem is a composite multi-objective optimization problemthat encompasses self-directed decision variables and multiple conflicting goals [30].Normal deviation practiced by ALO and GOA is lesser than the BBO and also other optimization systems. ALO andGOA might be impelled in diverse kinds of applications in the fields of electromagnetics. Unequivocally, they will beused in the mixture of other diverse frameworks of antenna arrays with detailed topographies [57].For finding the solution of extremely non-linear values of the existing excitation weights for the concentric circularantenna array synthesis with and without the Centre constituent an Ant lion optimization (ALO) system is pragmatic.By means of the ALO technique the problem of scheming CCAA with non-uniform current excitation weights andinter-element separations has been discovered.With the aid of pattern multiplication system a generic methodology is stated which could be found practical for anykind of antenna element. The attained results from this method are well equated with the other works in literature.This displays that the ALO algorithm supports an innovative CCAA system by means of SSL reduction converselyassisting a narrow FNBW. Correspondingly ALO maintains good prospective as an optimized method in terms ofpattern molding of a concentric circular antenna array [60].PreyA AFBCCDDE FGLeft wingwingCenterRightFigure 3. General Lion prey position3.EXISTING LION SWARM OPTIMIZATION TECHNIQUES3.1 Lion Optimization in Software Defined NetworksIn Software Defined Networks the Lion Optimization algorithm is well adopted for improving the network controlsystems. This is attained by the process of decoupling the control plane from the data plane. These networks furtherbecome heterogeneous in nature. The several applications over networks that stretch from wired and infrastructurebased wireless sensor networks reports this heterogeneity. In a wireless sensor network, numerous sensors will beconnected based on the applications and transfers proper data communication [61].An effective routing scheme based on SDN is determined for WSN depending on Lion Optimization algorithm. Thechief objective of this specific research work is to achieve reasonable energy efficiency by improving the networklifetime and QoS. The three major process initiated to attain the major objective is cluster formation, establishment ofroute and proper transmission of data. With the aid of LO algorithm the sensor nodes were gathered and instructionswere renowned to transfer those types of data [1].Volume 26, Issue 12, 2020456http://www.gjstx-e.cn/

High Technology LettersISSN NO : 1006-67483.2 Lion optimization for cluster head-based routing protocolWell established routing path should be maintained in wireless sensor networks for the process of transferring datapackets from source node to the destination node [62]. Fractional Lion (FLION) clustering algorithm is specified forcreating an uninterrupted routing path. In the instant of transmitting data from one location to other location, therouting path is preferred based on several factors such as delay, sufficient energy and distance. Suitable selection ofcluster head is a significant factor for electing routing path. Choosing the proper cluster head becomes a challengingtask in the process of data transmission in WSN. The proposed FLON aids to overcome this issue [4].3.3 Ant Lion Algorithm in Feature SelectionIn Feature Selection (FS) process, the size of the data will be further decreased by means of removing the incomparablefeatures present in the original datasets [63]. Adoption of feature selection with grouping algorithm improves theclassification accuracy and reduces the computational processing time period [6].In FS based concerns, N sized binary vector is been implied,Where,𝑁 signifies the total number of features in an available dataset.The difficulty of creating all conceivable feature selection combinations would be well-defined by 2 𝑁Where, a brute-force searches turns out to be impractical.Meta-heuristics are further consistent solutions for such problems and among them; ALO is one of the meta-heuristicapproach that shows a worthy performance in examining the feature space for the outstanding feature subclass [64].3.4 Discrete Ant Lion Optimization (DALO)Among the broad view of optimization concerns, the Travelling Salesman Problem (TSP) is recognized as a vitalfactor to be resolved scientifically in numerous applications. The data gathering tour problem is well suitably confinedto symmetric Euclidean TSP. Discrete Ant Lion optimization is been suggested as an alternate for solving datagathering tour problem in networks. This gathers information from all the existing sensor nodes in a wireless sensornetwork by dropping the cost of data assembly [7]. Mobile data assembly could be designated over demandable da

wide applications of Lion swarm optimization algorithm in wireless sensor network are discussed in detail. KEYWORDS--- energy efficiency, Lion swarm optimization, Wireless Sensor Networks. 1. INTRODUCTION Wide range of assembly of interconnected sensors surrounded by the wireless medium is encompassed in

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