A Novel Energetic Ant Optimization Algorithm for Routing Network Analysis

  • Xiang Feng
  • Hanyu Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)


The latest biological research results show that it is natural to see that ants at different age group play roles and responsibilities differently. As inspired by the same, the concept of age and intra-groups is thus introduced into traditional Ant Colony Optimization (ACO) algorithm. A new intelligent parallel algorithm, Energetic Ant Optimization model (EAO), is put forward and applied for energy-aware routing network analysis. The proposed algorithm is designed to calculate the routing probability and phenomenon increment by taking the remaining energy of node as a heuristic factor. By EAO, the age of ant corresponds to the energy of the Ad Hoc network. Not only was mathematical model built for the EAO theoretically, but also its application was described detailedly. Finally, the proposed algorithm is simulated and analyzed in different scenarios, and the experimental results are compared with the results of Ad hoc on-demand distance vector routing (AODV). The simulation results show that EAO routing algorithm (EAORA) performs much better in packet delivery ratio, the average end-to-end delay and lifetime of network. Besides, the EAORA has better performance in balancing the energy consuming between nodes.


Energetic ant optimization model Routing network analysis Energy-aware 



This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61472139 and 61462073, the Information Development Special Funds of Shanghai Economic and Information Commission under Grant No. 201602008, the Open Funds of Shanghai Smart City Collaborative Innovation Center.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringEast China University of Science and TechnologyShanghaiChina
  2. 2.Smart City Collaborative Innovation CenterShanghai Jiao Tong UniversityShanghaiChina

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