Effective Modeling of Photovoltaic Modules Using Sailfish Optimizer

Abstract

The current study proposes a novel meta-heuristic technique called sailfish optimizer (SFO) to design reliable photovoltaic (PV) modeling models. Unlike others, the proposed technique employs two populations (prey and predator) instead of one to effectively reach the desired solution. This unique propriety can substantially augment the probability of locating the global optimum as well as accelerating the search process. Moreover, to show the efficacy of the algorithm, the results are compared with some literature techniques such as Salp-Swarm-Optimizer (SSA), Whale Optimization (WOA), Artificial-Bee-Colony (ABC), and Particle-Swarm Optimization (PSO) methods. Eventually, the proposed SFO algorithm demonstrated a remarkable amelioration in terms of accuracy with Root-Mean-Square-Error of 13E-3 A