A Multi-objective Approach for Energy Efficient Clustering Using Comprehensive Learning Particle Swarm Optimization in Mobile Ad-hoc Network

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 multiobjective 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 multiobjective comprehensive learning particle swarm optimization (MOCLPSO) which reduces the timecomplexity 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.