Total views : 270

Efficient Mobile Agent Path-search Techniques using Genetic Algorithm Processing


  • Department of Automotive Software, Youngdong University, Korea, Republic of
  • Department of National Defense Intelligence Engineering, SangMyung University, Korea, Republic of


Background/Objectives: Although the efficiency of genetic algorithms improves with the creation of each generation, a number of generations are needed to obtain the desired results. In addition, when ad hoc unit increases are linked to a network, it may be necessary to compare all cases. Methods/Statistical Analysis: This requires the simultaneous generation of multiple algorithms at one time. Where a single process is used to manage all such algorithms, the overall efficiency of the network will decrease. Findings: The algorithm proposed in this thesis introduces router group cell units for use in the distributed processing of previous genetic algorithms. The experimental results showed that the proposed algorithm reduced path processing costs caused by the alternative path setup by approximately 27% when compared with Dijkstra's and the Munetomo algorithm. Operation time for the alternative path setup was approximately twice as fast as that of Dijkstra's algorithm. These results suggest that the algorithm proposed in this paper is more efficient than either Dijkstra's or the Munetomo algorithm in terms of alternative path setup during router failure. Application/Improvements: The study presents ways to reduce overall search delays across a network through the use of a cell-based genetic algorithm.


Ad-hoc Network, Genetic Algorithm, Mobile Agent, Path-search Algorithm, Route Search Method.

Full Text:

 |  (PDF views: 191)


  • Persis DJ, Robert TP. Ant based multi-objective routing optimization in mobile ad-hoc network. Indian Journal of Science & Technology. 2015; 8(9):875–88.DOI: 10.17485/ ijst/2015/v8i9/59369.
  • Begen C, Akgul T, Baugher M. Watching video over the web part 1: streaming protocols. IEEE Internet Computing. 2011; 15(2):54–63.
  • Floyd S, Ratnasamy S, Shenker S. Modifying TCP’s congestion control for high speeds [Internet]. 2002 May.Available from:
  • Holland JH. Adaptation in natural and artificial systems. The MIT Press; 1992.
  • Karnik NM, Tripathi AR. Design issues in mobile-agent programming systems. IEEE Concurrency. 1998; 6(3):52– 61.
  • Baumann J, Hohl F, Rothermel K, Strasser M, Theilmann W.MOLE: A mobile agent system. Software: Practice and Experience. 2002; 32(6):575–603.
  • Inagaki J, Haseyama M, Kitajima H. A genetic algorithm for determining multiple routes and its applications. ProceedingsIEEE International Symposium on Circuits and Systems. 1999;6:137–40.
  • Ahn C, Ramakrishna RS. A genetic algorithm for shortest path routing problem and the sizing of populations. IEEE Transactions on Evolutionary Computation. 2002; 6( 6):566–79.
  • Liu B, Choo S, Lok S, Leong S, Lee S, Poon F, Tan H. Integrating case-based reasoning, knowledge-based approach and Dijkstra algorithm for route finding. Proceedings of the Tenth Conference on Artificial Intelligence for Applications. 1994;1:149–55.
  • Xi C, Qi F, Wei L. A new shortest path algorithm based on heuristic strategy. Proceedings of the Sixth World Congress on Intelligent Control and Automation. 2006;1: 2531–6.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.