期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2014
卷号：65
期号：3
出版社：Journal of Theoretical and Applied
摘要：A mobile ad-hoc network (MANET) faces various challenges including limited energy, limited communication bandwidth, computation constraint and cost. Therefore, clustering of sensor nodes is adopted which involves selection of cluster-heads for each cluster. This enhances system performance by enabling bandwidth reuse, better resource allocation and improved power control. The various existing clustering techniques provide a single optimized solution in a single simulation run. Therefore, a multi-objective approach is used to optimize the number of clusters and to manage the energy dissipation issues. The proposed algorithm is a multi-objective variant of Particle Swarm Optimization (PSO) called multi-objective comprehensive learning particle swarm optimization (MOCLPSO) which reduces the time-complexity and increases the speed of the algorithm. In this technique, the best position of a randomly selected particle from the population is used to update the velocity of particle in each dimension, rather than using the personal or global best positions. The parameters taken into consideration in the proposed algorithm includes degree of nodes, transmission range and battery power consumption of the nodes. This technique provides multiple trade�off solutions in a single run of the algorithm. The performance of the proposed algorithm is compared with various clustering techniques: LEACH, PSO, WCA, CLPSO and MOPSO.
