An Improved Firefly Algorithm Hybridized with Extremal optimization for Parameter Identification of Photovoltaic Models

Abstract

Firefly algorithm (FA) has widely used to solve various complex optimization problems. However, FA has significant drawbacks in slow convergence rate and easily trapped into local optimum. To tackle these defects, this paper proposes an improved FA combined with extremal optimization (EO), named IFA-EO, where three strategies are incorporated. First, to balance tradeoff between exploration and exploitation, we adopt a new attraction model for FA operation, which combines the full attraction model and the single attraction model through the probability choice strategy. In single attraction model, inspired by the simulated annealing idea, small probability accepts the worse solution to improve the diversity of the offspring. Second, the adaptive step size is proposed according to the number of iterations. Third, we combine EO algorithm with powerful ability in local-search. IFA-EO is employed to handle three different parameters identification problems of photovoltaic model. For comparisons, we choose three swarm intelligence algorithms to compare with IFA-EO. Simulation results demonstrate the superiority of IFA-EO to other three competitors.