Clustering in mobile ad hoc networks with differential evolution

This paper presents a new, differential-evolution-based method for solving the problem of optimal selection of cluster-heads and cluster-members in mobile ad hoc networks. A novel encoding scheme is used to represent nodes in the network graph, and randomly-generated networks of different sizes are solved. The present method handles problems of much larger sizes than do the best-known methods in the literature. Empirical results show the superiority of this method over state-of-the-art approaches on two counts: quality of the solution and time to find the solution.

[1]  Sajal K. Das,et al.  A Study of k-Coverage and Measures of Connectivity in 3D Wireless Sensor Networks , 2010, IEEE Transactions on Computers.

[2]  Kalyanmoy Deb,et al.  Analysis of Selection Algorithms: A Markov Chain Approach , 1996, Evolutionary Computation.

[3]  Sajal K. Das,et al.  Energy-Efficient Reprogramming of a Swarm of Mobile Sensors , 2010, IEEE Transactions on Mobile Computing.

[4]  Jiannong Cao,et al.  Stability-aware multi-metric clustering in mobile ad hoc networks with group mobility , 2009 .

[5]  Sajal K. Das,et al.  WCA: A Weighted Clustering Algorithm for Mobile Ad Hoc Networks , 2002, Cluster Computing.

[6]  Béla Bollobás,et al.  Random Graphs , 1985 .

[7]  Ramez Elmasri,et al.  Optimizing clustering algorithm in mobile ad hoc networks using genetic algorithmic approach , 2002, Global Telecommunications Conference, 2002. GLOBECOM '02. IEEE.

[8]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[9]  Amit Konar,et al.  Differential Evolution Using a Neighborhood-Based Mutation Operator , 2009, IEEE Transactions on Evolutionary Computation.

[10]  Abhishek Roy,et al.  QM2RP: A QoS-Based Mobile Multicast Routing Protocol Using Multi-Objective Genetic Algorithm , 2004, Wirel. Networks.

[11]  Ramez Elmasri,et al.  Optimizing clustering algorithm in mobile ad hoc networks using simulated annealing , 2003, 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003..

[12]  Sajal K. Das,et al.  Mission-Oriented k-Coverage in Mobile Wireless Sensor Networks , 2010, ICDCN.

[13]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[14]  Zbigniew Michalewicz,et al.  Genetic algorithms + data structures = evolution programs (3rd ed.) , 1996 .

[15]  Uday K. Chakraborty,et al.  Advances in Differential Evolution , 2010 .