Comparative Study of PSO and ABC Algorithms for Finding Base-Station Locations in Two-Tiered Wireless Sensor Networks

Abstract

Recently, several modern heuristic algorithms have been developed for solving combinatorial and optimization problems. These algorithms can be classified into different groups depending on the criteria being considered, such as population based, iterative based, stochastic, deterministic, etc. Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees’ swarming around their hive is another example of swarm intelligence. Particle swarm optimization (PSO) is a popular multidimensional optimization technique which models social behavior of a flock of birds & Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. In this work both PSO and ABC algorithms are applied to find nearly optimal BS locations in heterogeneous sensor networks, where application nodes may own different data transmission rates, initial energies and parameter values. Experimental results show the performance comparison of the proposed PSO & ABC approaches. The proposed algorithms can thus help finding good BS locations to reduce power consumption and maximize network lifetime in two-tiered wireless sensor networks.