International Journal of Advanced Studies in Computer Science and Engineering Ijascse Volume 5 Issue 11, 2016

Swarm robotics is a new area of interest that’s inspired from biological system of ants, birds and other social systems for solving different optimization problem. This paper work out to find the optimal path using particle swarm optimization algorithm and dijkstra’s algorithm, these algorithms are used to find the shortest path between different nodes. Different path can exist from one node to another node, particularly combinational optimization algorithm are used to solve such optimization problem. Different algorithms are used to solve the given problem. The PSO repeatedly find out the better solution for the given problem. The dijkstra’s algorithm used to check the convenience path between different nodes. Problem arise in PSO of flout delay cost at each node where the dijkstra’s keep the delay cost at each node with some specific value. Our current work propose the approach to merge the PSO and Dijkstra’s algorithm to provide new method of solving the optimization problem.) KeywordsDijkstra’s; Particle Swarm Optimization; Optimization problem; swarm intelligence and Network Routing. INTRODUCTION: PSO is firstly inspired by the general artificial science, like fish schooling, bird flocking and social communication behavior of human and secondly by arbitrary search methods of evolutionary algorithm. The social organism communicate and interact with each other. Some of the properties of swarm intelligence are decentralized, selforganized system, artificial acts. In the given setup of swarm robotics individual entity combines to work together, these agents interact locally with one another and with their environment. Different agents communicate together and respond to the change occur in environment. The agent’s communication create global intelligent behavior, and the system becomes decentralized in nature. Different swarm intelligence algorithm exists like fish schooling, bird flocking [1], bacteria growth and ant colonies [2] [3] etc. Swarm intelligence is based upon the characteristics like no centralization, robustness and flexible system [4]. Different activity is perform in the natural swarms like construction of nest, [5] allocation of different activities, [6] path planning, [7] another individual tasks etc. [8] [9] [10]. Since the swarm robotics is mostly inspired from the nature swarms, it’s a good reference for analyzing the characteristics of nature swarms. The research of swarm robotics started a century ago. The first hypothesis is quite personified [11] and assumes that each individual has a unique ID for cooperation and communication. The INTERNATIONAL JOURNAL OF ADVANCED STUDIES IN COMPUTER SCIENCE AND ENGINEERING IJASCSE VOLUME 5 ISSUE 11, 2016 11/30/2016 WWW.IJASCSE.ORG 35 information exchange in the swarm is regarded as a centralized network. The queens in ant and bee colonies are supposed to be responsible for transmitting and assigning the information to each agent [12] . However, Jha, et al. [13] proved that the network in the swarm is decentralized. Thanks to the research in recent half century, the biologists can now assert that there are no unique IDs or other globally storage information in the network. No single agent can access to all the information in the network and a pacemaker is therefore inexistent. The biologists now believe that the social swarms are organized as a decentralized system distributed in the whole environment which can be described through a probabilistic model [14]. The agents in the swarm follow their own rules according to local information. The group behaviors emerge from these local rules which affect information exchange and topology structure in the swarm. The rules are also the key component to keep the whole structure to be flexible and robust even when the sophisticated behaviors are emerged. Swarm robotics is comparatively new area that targets on governing large homogeneous simple physical robots. It is assumed that a well-combined attitude rise from the communication between robots and communication of robots with the surroundings. The path rise in the area of artificial swarm intelligence likewise biological study of insects, birds, ants and other area in the nature too, where the behavior of swarm occurs. These systems are developed and better at micro-level behaviors. These systems are made of simple and collection of small robots. Make colony and perform task collectively. It is the property of swarm robots to make population of many robots of hundreds of robots [15]. PSO is an empirical global optimization technique presented by Doctor Kennedy and E berhart in 1995. PSO is based upon two disciplines that are Computer science and social science [16]. Particle swarm optimization is new technique based on the swarm intelligence [17] [18] [19]. Different swarm intelligence models evolve some of them are fish schools, bird flocking, ant and bee colonies [11]. They search food, move in random directions. In bird flocking an individual bird having sense to find the food near to that bird. That bird contain heuristic information to find out the goal. In addition birds are sharing local information with each other, particularly better information instantaneously in search food in a specific area, the birds are inspiring from better information hence they flock toward that place where food can be found. PSO algorithm solve such problems using the same technique [20] [21]. Dijkstra’s algorithm is used for solving the shortest path finding problem, while there are not a negative weight present in all edges of the graph. Similar to Prims algorithm it is greedy in nature [22] [23] and start at origin vertex O. It develop a tree Y ,that eventually store all vertices reachable to origin O. Adding vertices to the tree following order of distance, O is added first then nearest vertex to O, then the next nearest and going on. I. LITERATURE REVIEW: Shortest path finding is hard combinational optimization problem, different area experts work to find out the optimized path for the given problem. Various solution for such type of problem are proposed using different techniques and optimized algorithms, for example kairanbay mazghan and hajar mat jani evaluate different algorithms like Floyd warshall, genetic, dijkstra’s and bellmen ford to solve the shortest path problem [24]. Their results concluded that Floyd warshall, dijkstra’s give the same results, but GA algorithm give better result as compare to these two different techniques. Kavitha Sooda T. R. Gopalakrishnan Nair worked on the comparative analysis of genetic and Particle swarm optimization algorithm to find the shortest path. They show that the merit of fitness value and number of hops individually in genetic algorithm and particle swarm optimization. Moreover they illustrate that the PSO produce better solution than genetic Algorithm [25]. Praveen Sharma and Neha Khurana studied different techniques like Dijkstra's Algorithm, Genetic Algorithm, Ant Colony Optimization and Hill climbing to find out the best possible optimal path and achieve better performance in the network. Their study shows that optimization problems can be concluded better by PSO and genetic algorithm rather than traditional techniques [26]. Further the Surbhi Jawa, Rajesh Malik present the new approach of avoiding the rerouting of network if the link between the source and destination become breakout. The current work propose the better approach using particle swarm optimization. The reliable network method is followed considering the energy cost limit as low as possible for the given network. Their results indicates that the offered approach has improve the network in term of energy and reliability. The proposed algorithm proposes to deliver secure network [27]. The paper [28] introduced an enhancement of the algorithm to operate on discrete binary variables. Eberhart and shi introduced the new parameter the inertia weight into the original particle swarm optimizer to improve the results [29]. Some theory based on particle swarm optimization is also discussed by the Vora and sexena. Their work presents the small world theory based upon the given method [30]. Particularly the dijkstra’s algorithm is also used in the application of human safety. The propose use of dijkstra’s algorithm to find the shortest path in the critical situation like fire. This intelligence algorithm provide the secure path for the rescue operations [31]. Literature work discuss that swarm intelligence solve different hard combinational optimization problem. This approach is numerous to use in different domain like medical image segmentation. Same type of INTERNATIONAL JOURNAL OF ADVANCED STUDIES IN COMPUTER SCIENCE AND ENGINEERING IJASCSE VOLUME 5 ISSUE 11, 2016 11/30/2016 WWW.IJASCSE.ORG 36 problem is discuss in paper [32] this novel approach propose the current algorithm to solve image segmentation problem. This proposed method was used in medical image segmentation and image compression with better results. In network terminology for the quality of the network, the hybrid PSO algorithm applied to improve performance [33]. Nowadays current network need to be intelligent in fact of usage reliability security and fast throughput paths. 1. PROBLEM STATEMENT: The shortest path finding in routing is tedious task. Various nodes are interconnected and create a link as path between them. Data moves from source to destination. In this given process, the data pass through several intermediate nodes to reach destination. Different paths exist from current node. The packet moves from source to destination this scenario based upon different parameter to keep, that may be time that is to take less time to reach to destination, security of packets, moment with less consumption of energy and finding better and optimize path. The energy and time consumption, is directly related to the path followed by the packet to deliver. The reduction of time and cost depends on path selection. The selection of path is tedious job. Example (checking all possible paths) takes too much time to calculate. Achieving high performance in the network using the optimal path which selected through Routing process. Literature study elaborate that various routing problems is solved by different methods. Various path may exist between two nodes and the most optimal path is selected on the basis of associated costs, it will considered as shortest path. Different types of algorithms are used for this problem, to select the shortest path in a converged network. In the given graph their exist a number of possible paths from start node ‘1’ to destination node ‘10’.But the problem is to find optimal path, so this problem can be addressed by PSO.As starting of PSO We have to initially define particles (means initially find random path from source to destination because every particle gives solution of problem).The number of particles searching in the solution space are directly related to optimize path finding. Figure1. Topology of converged network This logic gives the algorithm provides more accurate result if particle are spread out over the network. These initial paths break network in small search space. For example all particles are taken for this network (More number of particle give more accurate solution because more particles can cover more nodes and give more initial solution in all space). So particle value are...