## 1 Introduction  
Energy is an essential component of the universe and is considered one of the forms of existence. Energy is divided into two main types (renewable energy and non-renewable energy); non-renewable energy as fossil fuels has a terrible impact on the environment. Therefore, many nations tend to use renewable energy to produce their electricity. Solar energy is one of the primary and available renewable energy sources on the planet that has no pollution and easy installation as well as being inexpensive and noise-free. The need to add renewable energy sources is increased with the dramatic changes in electricity requirements. Therefore, the effective modeling of renewable energy resources is an important issue for efficient energy management [1]. Solar cells are one of the ways to take advantage of solar energy, so significant attention went to model photovoltaic (PV) cells [2; 3; 4; 5; 6; 7]. Several parameters define the nonlinear electrical model of a solar cell, which must be studied in depth to design PV systems. It is vital to understand the current-voltage graph (I-V) before using PV cells. In addition to determining PV's parameters, picking a few points from this curve can also help. Based on the number of diodes, different parameter models are presented. Three different types are available: single diode, double diode, and three diode [8; 9; 10; 11]. Parameter identification can be accomplished in two ways, using deterministic methods or using metaheuristics. Examples of traditional approaches are Lambert W-functions [12] and the interior-point method [13]. Although traditional models can solve parameter identification, it has some drawbacks facing nonlinear problems such as sensitivity to the initial solution besides sticking in a local optimum solution with heavy computations and taking a long time to reach this optimum. Therefore, metaheuristics algorithms are used to overcome these drawbacks. Examples of these metaheuristics are the Particle Swarm Optimization (PSO) [6], Genetic Algorithm (GA) [14], Differential Evolution (DE) [15], Harmony Search (HS) [16], Artificial Bee Colony (ABC) [17], and Simulated Annealing (SA) [18]. The continuous development in optimization methods has been notable in recent decades. For example, several optimization methods were developed and applied for different power system problems, as presented in [19; 20]. Furthermore, in [21; 22; 23; 24; 25], an algorithm that mimics the elephant herding behavior called Elephant Herding Algorithm (EHO) was proposed for different applications. Reference [26] proposes three improved variants of EHO that are developed. The basic architecture of the PV cell guarantees that two differentially doped semiconductor layers form a PN junction. When irradiation is present, the cell absorbs photons from incoming light and produces carriers (or electron-hole pairs). As a result, there may be a discrepancy at the intersection [27]. In an ideal PV cell model, a photocurrent source and a diode are connected in parallel. Model estimation is made easiest by the fact that there are only three unknown parameters: the ideality factor \(\eta\), the photocurrent \(I_{pv}\), and the reverse saturation current \(I_{s}\). The contact resistance \(R_{s}\) between the silicon and electrode surfaces is described by this resistance. A parallel resistance \(R_{p}\) is attached to the diode to prepare for leakage current in the PN junction. The single-diode model (SDM) model has five parameters that must be estimated: \(I_{pv}\), \(I_{s}\), \(R_{s}\), and \(R_{p}\)[28]. The double-diode model (DDM) is a more precise method of modeling PV cells. It takes into account current loss recombination in the depletion area. With the addition of the seventh parallel diode, there are now seven parameters to estimate (\(I_{pv}\), \(\eta_{1}\), \(I_{dl}\), \(\eta_{2}\), \(I_{d2}\), \(R_{p}\), and \(R_{s}\)) [8]. These models are of great interest to many researchers.