An empirical study on differential evolution for optimal power allocation in WSNs

Optimal power allocation (OPA) is considered to be one of the key issues in designing a wireless sensor network (WSN). Generally, the OPA in WSN can be formulated as a numerical optimization problem with constraints. Differential evolution (DE) has been proved to be a very efficient evolutionary algorithm for global numerical optimization. Based on this consideration, in this paper, an empirical study is conducted to evaluate the performance of DE for the OPA. Different mutation strategies and different parameter settings are used in DE to test the influence of them when optimizing the OPA. The sensors in WSN are scaled up to 200. Experimental results indicate that the parameters of CR and F are sensitive to the performance for the OPA. In addition, with the same parameter setting, the strategy selection is stable in DE.