Dynamic genetic algorithms for the dynamic load balanced clustering problem in mobile ad hoc networks

  • Authors:

  • Hui Cheng;Shengxiang Yang;Jiannong Cao

  • Affiliations:

  • Department of Computer Science and Technology, University of Bedfordshire, Luton LU1 3JU, UK;School of Computer Science and Informatics, De Montfort University, Leicester LE1 9BH, UK;Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2013

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Abstract

Clustering can help aggregate the topology information and reduce the size of routing tables in a mobile ad hoc network (MANET). To achieve fairness and uniform energy consumption, each clusterhead should ideally support the same number of clustermembers. However, a MANET is a dynamic and complex system and its one important characteristic is the topology dynamics, that is, the network topology changes over time due to the factors such as energy conservation and node movement. Therefore, in a MANET, an effective clustering algorithm should efficiently adapt to each topology change and produce the new load balanced clusterhead set quickly. The maintenance of the cluster structure should aim to keep it as stable as possible to reduce overhead. To meet this requirement, the new solution should keep as many good parts in the previous solution as possible. In this paper, we first formulate the dynamic load balanced clustering problem (DLBCP) into a dynamic optimization problem. Then, we propose to use a series of dynamic genetic algorithms (GAs) to solve the DLBCP in MANETs. In these dynamic GAs, each individual represents a feasible clustering structure and its fitness is evaluated based on the load balance metric. Various dynamics handling techniques are introduced to help the population to deal with the topology changes and produce closely related solutions in good quality. The experimental results show that these GAs can work well for the DLBCP and outperform traditional GAs that do not consider dynamic network optimization requirements.