Hybridizing Biogeography-Based Optimization With Differential Evolution for Optimal Power Allocation in Wireless Sensor Networks

This paper studies the performance of a wireless sensor network (WSN) in the context of binary detection of a deterministic signal. This paper aims to develop a numerical solution for the optimal power allocation scheme via a variation of the biogeography-based optimization (BBO) algorithm, which is called the constrained BBO-DE algorithm. This new stochastic optimization algorithm is a hybridization of a very recently proposed stochastic optimization algorithm, i.e., the BBO algorithm, with another popular stochastic optimization algorithm called the differential evolution (DE) algorithm. The objective is to minimize the total power spent by the whole sensor network under a desired performance criterion, which is specified as the detection error probability. The proposed algorithm has been tested for several case studies, and its performances are compared with those of two constrained versions of the BBO and DE algorithms.