New variants of the differential evolution algorithm: Application for neuroscientists

When dealing with non-linear estimation issues, metaheuristics are often used. In addition to genetic algorithms (GAs), simulating annealing (SA), etc., a great deal of interest has been paid to differential evolution (DE). Although this algorithm requires less iterations than GAs or SA to solve optimization issues, its computational cost can still be reduced. Variants have been proposed but they do not necessarily converge to the global minimum. In this paper, our contribution is twofold: 1) we present new variants of DE. They have the advantage of converging faster than the standard DE algorithm while being robust to local minima. 2) To confirm the efficiency of our variants, we test them with a benchmark of functions often considered when studying metaheuristic performance. Then, we use them in the field of neurosciences to estimate the parameters of the Hodgkin-Huxley neuronal activity model.