Intelligent routing approaches using Bacteria Foraging Algorithm and Artificial Bee Colony

Transcription

1 Intelligent routing approaches using Bacteria Foraging Algorithm and Artificial Bee Colony T.R. Gopalakrishnan Nair 1, Kavitha Sooda 2, Bharat Alva K 3 1 Saudi ARAMCO Endowed Chair Technology, PMU, KSA Advanced Networking Research Group VP, Research, Research and Industry Incubation Center (RIIC), Dayananda Sagar Institutions, Bangalore , India 2 Advanced Networking Research Group (RIIC), DSI, and Assoc. Professor, Dept. of CSE Nitte Meenakshi Institute of Technology, Bangalore , India 3 Dept. of Computer Science & Engineering, Nitte Meenakshi Institute of Technology, Bangalore , India trgnair@ieee.org, Abstract: Networks today are continuously changing and network routing is a major challenge. This is due the fact that the networks complexity is increasing and predominant in today s internet. To cope up with this problem we use the advanced concepts of artificial intelligence and give a cognitive approach to the issues faced in networking. In our paper we give the detailed description of the implementation of the two algorithms namely Bacteria Foraging Algorithm (BFA) and Artificial Bee Colony (ABC). These algorithms were chosen to analyze how foraging algorithm and evolutionary algorithm run on network routing setup. We notice that when we use the BFA in our network setup we obtain an optimal path and then on the same setup if we run ABC algorithm we get an optimal path with better credentials. The focus here was to find the optimal path based on the bandwidth. We studied the setup for convergence rate, hop count and optimal cost found by the algorithms. We found that ABC was better than BFA in terms of the three factors considered above in the simulation results. Keywords Artificial Bee Colony (ABC), Bacteria Foraging Algorithm (BFA), Chemotaxis, Optimal path; Routing; Unemployed Bees;. I. INTRODUCTION Routing is a selection process, where the best path between two nodes in the network is chosen for the transmission of data [1]. There may exist a numerous amount of paths between nodes, when the objective is given to route the data between two specific nodes namely the source (where the transmission starts) and the destination (where the transmission ends).usually data which is sent contains some meaningful and valuable information, so faster transmission of data ensures increase in efficiency. In order to do this, routing is done with the help of well-constructed algorithms so that the efficient path between the source and the destination is found by calculating the cost taken in a particular path. There are number of algorithms which are designed for routing purposes such as Dijkstra s algorithm [2], which provide us a methodology for routing but the restriction to only one path causes a drawback. There are also other evolutionary algorithms like Genetic algorithms (GA) [3] and Particle Swarm Optimization (PSO) [4] algorithms which can be used for path determination. Bacteria Foraging Algorithm (BFA) is an Artificial Intelligence (AI) algorithm which exhibits the nature of bacteria in order to obtain the optimal path by considering the cost found by bacteria while foraging. The path with the best cost is taken as the optimal path and is named as the output of the algorithm. Another AI algorithm is the Artificial Bee colony (ABC) algorithm where the nature of bee is mimicked and the routing is done by exhibiting the roles and characteristics of the bee. The optimal path is once again found by taking the best cost found and also proximity into consideration. Here in this paper, we exhibit the performance of the two algorithms namely BFA and ABC by setting up a region based network. For the given source and destination we show how the routing is done by these algorithms and the paths taken by them along with the cost. We draw a comparison between the two algorithms by taking the parameters of optimal cost, hop count and convergence rate and we depict the better of the two algorithms. Repetitive runs have proved that the cost, hop count and convergence rate found by ABC is better than BFA and so we can say that the ABC outperforms BFA and is the better algorithm of the two. The rest of this paper is organized as follows: The next section discusses the related work, followed by the description of the system module setup in section III. Section IV has the simulation results. The conclusion and future work are discussed in Section V. 37

2 II. RELATED WORK A. BACTERIA FORAGING ALGORITHM (BFA) One of the featured concepts in Artificial Intelligence are the Bio- inspired Algorithms. Bio-inspired algorithms are the ones which are based on the behavior of animals, insects and micro-organisms, study their behavior and implement it in computing scenarios. Ants, Bees, birds, Termites, etc. are some of the general organisms from which algorithms are drawn. Bacteria Foraging Algorithm (BFA) is also a bio-inspired algorithm which is drawn from the prospectus of bacteria. BFA mimics the nature of bacteria, how they interact with obstacles and behave in the environment [5]. reproduction helps us to find more solutions and multiplies the possibilities of finding solutions which are probably more optimal than the ones that are already found. Finally the elimination dispersion step, which deals with the elimination of the solution found by bacteria that are not optimal and retaining only those which are. These three steps constitute the bacteria foraging cycle and they form the process which the bacteria takes to find solutions to the problem [8]. Our project deals with the implementation of BFA in wired networks, the bacteria foraging algorithm is implemented in simulated network environment and is used to find the optimal path between the source and the destination. Bacteria are one of the well-understood microorganism, taking into consideration the e-coli bacteria is a very peculiar specimen, primarily because of its body structure. The body structure of the e-coli bacteria typically consists of a small biological motor and there is threadlike structures that are connected to the motor called flagella. The spinning of these flagella causes the bacteria to move, the movement are of two types i.e. they can either swim or Tumble [6]. The swim is usually caused by the spinning of the flagella in the anti-clockwise direction and this is done when the bacteria needs to travel in a particular direction and this motion is directed and is fast. The tumble is another type of bacterial movement and this is caused when the bacteria encounters an obstacle in its path, the bacteria slow down by spinning the flagella in the clockwise direction and after the tumble the bacteria is usually placed in a direction where it then continues its swim. The objective of these bacteria is to find nutrient concentration, the foraging for nutrients is made by alternating between swim and tumble [7]. The bacteria are initially present in a swarm and then they go in all possible directions to find the food i.e. they scatter. BFA is an algorithm derived from the nature of bacteria and it concentrates on the technique which they use to find nutrients. The foraging of bacteria is split up into three steps, they are- chemotaxis, reproduction and elimination-dispersion. The first step of chemotaxis deals with the bacteria s being cast out from the swarm in all possible directions and each of the bacteria take their own random way in search of the nutrient concentration and the bacteria carry out this process by using the two of its motion i.e. swim and tumble. They swim in a particular direction and when they encounter an obstacle they tumble which causes them to change their direction and be put in another random direction where they continue their search by swimming. After the completion of a particular chemotaxis cycle the healthy part of the bacteria are reproduced, Fig 1: Flowchart for BFA Intelligence routing takes place, the paths between the source and the destination are found by chemotaxis. The parameter considered here is bandwidth, higher bandwidth means higher concentration i.e. nutrient concentration and more the tendency of the bacteria to find the destination in that direction and after a particular bacteria finds the destination the path, cost taken and hop count is noted. Then reproduction is done in such a way that a particular path found by the bacteria is eliminated and the chemotaxis of the rest 38

3 of the bacteria is considered and this will give us more paths to the destination. Finally in the elimination step the path with the optimal cost (highest cost) is retained and the hop count is displayed and considered as the output of BFA [9]. B. ARTIFICIAL BEE COLONY (ABC) The Artificial Bee Colony (hereafter referred to as ABC) is another bio-inspired algorithms which mimics the behavior of bees in the environment. Unlike BFA, the ABC algorithm deals with the nature of comparatively complex organism namely bees. Bee behavior have been studied since a long time and it fascinates us that the interaction and coordination of the bees bring to light a very complex society. Just like ant s they exhibit discipline and hierarchy in their system and constitute multiple roles. When a particular part is assigned to a bee it is limited to its actions and has to fulfil the responsibilities given to it. The bee consists of a colony and this consists of a swarm of bees present in the hive. The bee colony drives on two characteristic: Self organization and division of labor [10]. Self - organization deals with the organized way of handling tasks i.e. performing one job after the other maintaining consistent co-ordination by communication. Division of labor deals with the ways in which the bees perform a particular job out of the entire work i.e. they maneuver the way of carrying out their job. There are various roles given to the bees and they fall into two categories they are: Unemployed bees and Employed bees. The Unemployed bees consists of two types, scout bees and onlookers. The scout bees have the job of searching all possible sources of food and they scatter in all direction from the hive and they report back to the hive, they see what sources are close to the hive and the amount of nectar (food) they have and they collect this information and report back to the hive. They convey the information collected in a form of dance called the waggle dance and this is performed when they get back to the hive. Onlookers are the ones who collect the information from the scout bees and they go in search of food, they make note of the paths taken and decide the best path for employed bees to gathering nectar (food) [11]. The decision is made by considering fair number of attributes such as proximity of the nectar source, the richness of the source. The onlooker bees take the decision and ones they have come up with a particular path they allow the employed bees to take the particular source and they carry out the duty of collecting nectar. When the source gets exhausted the particular path is eliminated and further paths to new sources are explored. Moreover, the employed bees to the particular source become scout bees when the quantity of nectar is exhausted. Thus the functioning of the bee colony. ABC is an artificial intelligence algorithm derived from the nature of bees and it portrays the nature and its implementation in computing [12]. ABC is designed according to the foraging behavior of bees. Our project deals with the implementation of ABC in wired simulated network the bee foraging technique is used to route the data from source to destination. Initially, the scout bee nature is brought into action, the primary details of the system are collected by the bees and they are stored. Secondly in order to exhibit the onlooker part we use the system information collected by the scout bees and draw the possible paths from the source to the destination [13]. Then the onlooker bees scatter into all these paths and they find out the best path, they do this by calculating the cost taken by them while traversing each path and they come out with the optimal cost and that path is taken. Fig 2: Flowchart for ABC The parameter on which they abide is bandwidth, the more the bandwidth the more prone they are to take that path. Proximity is exhibited by keeping a controlling parameter i.e. threshold and when the bandwidth is 39

4 below the threshold then they cannot take the particular path. Finally, when the cost of all possible paths is calculated, the best path is taken as the optimal one and the path is declared as the optimal path between the source and the destination. The ABC provides us by giving a good enough outcome and an efficient route and from this the hop count is calculated and displayed. III. SYSTEM MODULE SETUP A region based network is one where the entire network is spread over a number of regions, consisting of nodes. Each region consists of a particular number of nodes. The nodes present within each region may be interconnected with each other (local nodes) or with the nodes of a different region. The number of regions along with the number of nodes in each region can be determined as follows, = / The links are made in a random way such each and every node has a link to some other node in its region or in another region and is not left isolated [14]. IV. SIMULATION RESULTS Initially the region based network setup is established, which forms as a base to run both our algorithms and compare their outcome. Once the setup is ready the source and the destination node are provided as the mandatory input. BFA algorithm takes the particular input and makes the computation by executing its three steps and gives us the output by telling us the various paths found and which path is selected as the most optimal one based on bandwidth collected from the nodes and the cumulative cost taken by it along with the time consumed by the algorithm and the hop count as shown in figure 4. Where, 2^a <pn<2^b Here a is the number of regions in the network and pnr is the number of nodes in a region. If the pnr value is a fraction, then the extra node is placed in the last region. For example, if pn = 28, here 2^4<28<2^5, we have 4 regions (a = 4)as shown in Figure 3. Fig 4: BFA Implementation Fig 3: Region Based network The next algorithm is ABC which is also run on the same setup. The algorithm performs its operation by taking into consideration the threshold (controlling parameter) into account and if any path violates the threshold condition then the particular path is eliminated. The optimal path along with cost collected by the cumulative bandwidth whose information is collected from the nodes and the hop count taken is displayed also the time taken to run the algorithm is also displayed as shown in figure 5. Now using Equation (1), 21/4=5.25 (pnr = 5.25). Since pnr is a fraction, each region will contain 5 nodes and the extra node (i.e. the 21st node) will be placed in the last region i.e., the fourth region in this case. 40

5 Fig 5: ABC Implementation The comparison between the two algorithms is drawn according to the three common factors obtained by running the two algorithms i.e. hop count, convergence rate and cost. These three factors will provide a clear comparison to how the two algorithms have performed on the given input. Fig 6: Comparison of BFA and ABC This is how the comparison is drawn in the form of a bar graph as shown in figure 6. V. CONCLUSION The results obtained show us that for the simulated setup of the region based network ABC outperforms BFA. This is because the path concluded by ABC has lesser cost than BFA. ABC converges at a faster rate and has a lesser hop count than the hop count of the path obtained by BFA. But in some situation as there is a controlling parameter in ABC we may not find any optimal path, in this case it is said that BFA outperforms ABC and is considered as the worst case. Further these two algorithms may be improved by considering more networking parameters. Acknowledgment Our thanks to Rakesh M, Anupam Pramanick and Aseem Saxena for providing ideas towards the work. [1] Yoshida M, Miki T. A routing Protocol for Wired-gum- Wireless Ad-Hoc Networks, Asia-Pacific Conference on Communications, pp: , October [2] Dijkstra, E. W A note on two problems in connection with graphs, Numerische Mathematik 1(1959), [3] T R Gopalakrishnan Nair, Kavitha Sooda and R Selvarani, A QoS based Routing Approach using Genetic Algorithms for Bandwidth Maximization in Networks, Int. J. of Artificial Intelligence and Soft Computing(IJAISC), Vol.4, Issue.1, pp.80-94, February 2014, DOI: /IJAISC [4] T. R. Gopalakrishnan Nair, Kavitha Sooda, Particle Swarm Optimization for Realizing Intelligent Routing in Networks with Quality Grading, WiCOM, 7 th IEEE International Conference on Wireless Communications, Networking and Computing, Wuhan, China, th September, pp. 1-4, 2011, ISSN : Print ISBN: , INSPEC Accession Number: DOI : /wicom [5] Wael Korani Bacterial Foraging Oriented by Particle Swarm Optimization Strategy for PID Tuning, International IEEE symposium on computational intelligence in Robotics in Automation 2009 pp [6] Kevin M Passino Bacteria foraging optimization, International journal of swarm intelligence research.1(1),1-16,january-march [7] Sambarta Dasgupta, Swagatam Das, Ajith Abraham and Arijit Biswas Adaptive Computational Chemotaxis in Bacterial Foraging Optimization: An Analysis, IEEE Transactions of Evolutionary computation, Vol. 13, NO. 4, August [8] Swagatam Das, Sambarta Dasgupta, Arijit Biswas, Ajith Abraham, and Amit Konar On Stability of the Chemotactic Dynamics in Bacterial-Foraging Optimization Algorithm, IEEE Transactions of Systems, Man, and Cybernetics Part A: Systems and Humans, Vol. 39, No. 3, May [9] Y. Liu and K. M. Passino Communicated by M. A. Simaan. Biomimicry of Social Foraging Bacteria for Distributed Optimization: Models, Principles, and Emergent Behaviors, Journal of optimization theory and applications: Vol. 115, No. 3, pp , December [10] Dervis Karaboga An idea based on Honey bee swarm for numerical optimization, Technical Report-TR06, October, [11] ] D Karaboga, B Basturk On the performance of artificial bee colony (ABC) algorithm, Technical Report-TR-38039, June [12] D Karaboga and B Akay A comparative study of artificial bee colony algorithm, Appl. Math. Computer Science, vol.214, pp , Aug [13] D Karaboga and B Basturk A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, J.Global Optim, Vol. 39,pp ,2007 [14] Kavitha Sooda, T. R. Gopalakrishnan Nair A Comparative Analysis for Determining the Optimal Path using PSO and GA, International Journal of Computer Applications ( ),Volume 32, No.4, October