Impact of Improved Chicken Swam Optimization Based A* algorithm In MANETs Routing
Gondi Yasoda Devi1, Gurrala Venkateswara Rao2
1Gondi Yasoda Devi*, Research Scholar, Department of CSE GIT, GITAM Deemed to be University, Visakhapatnam, India (Faculty Member Department of CSE, Lendi Institute of Engineering & Technology, Vizianagaram)
2SDr. Gurrala Venkateswara Rao, Department of CSE, GIT, GITAM Deemed to be University, Visakhapatnam, India.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 2176-2186 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3722129219/2019©BEIESP | DOI: 10.35940/ijeat.B3722.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Wireless devices utilization had increased drastically, which has shown an impact on over-all demand and utilization Mobile Ad-Hoc Network (MANET). Routing protocol is the fundamental and vital performance factor in the Mobile Ad-hoc Network (MANET). The routing protocols in MANET are accomplished to handle a lot number of nodes with restricted resources. Multiple routing protocols exist in MANETs. Once of the main challenges in routing protocols is its generation of adverse influence on network performance. Accordingly, this paper plans to implement an obstacle-ware MANET routing model using improved meta-heuristic-based A* algorithm. The algorithm efficiently plots a path between multiple nodes avoiding obstacles, or points, on the graph that results in producing a shortest path without any obstacles. The improved meta-heuristic algorithm termed as Fitness and Position Ratiobased Chicken Swarm Optimization (FPR-CSO) is used to improvise the A* algorithm. The comparative analysis of different optimized A* over Ad hoc On-Demand Distance Vector (AODV) confirms the consistent performance of the proposed model.
Keywords: MANET Routing; Optimal Shortest Path; Obstacle Aware Routing; A* Algorithm; Fitness and Position Ratio-based Chicken Swarm Optimization.