No abstract

The elephant herding optimization (EHO) algorithm is a relatively novel population-based optimization technique, which mimics herding behavior and can be modeled into two operators: clan updating operators and separating operators. Also, in the literature, EHO has received a great deal of attention from researchers since it was proposed applied to many application fields for its advantages of excellent global optimization ability and ease of implementation. However, there is still an insufficiency in the EHO algorithm regarding its lack of exploitation, which leads to slow convergence. In this paper, we propose three enhanced versions of EHO based on the γ value termed EEHO15, EEHO20, and EEHO25 to overcome the problems of fast unjustified convergence toward the origin of the basic EHO. The exploration/exploitation abilities of the EEHO algorithms are achieved by the updating of the two operators (clan and separation operator). To tackle this drawback, a constant function is used as a benchmark for inspecting the biased convergence of evolutionary algorithms in general. Moreover, we utilize CEC'17 test suite benchmark functions to test the performance of the proposed three versions of EEHO against EHO, particle swarm optimization (PSO), bird swarm algorithm (BSA), and ant lion optimizer (ALO) algorithms. Eventually, the experimental results revealed that the proposed EEHO algorithms extremely obtained better results compared with other competitive algorithms.

Recently, the resources of renewable energy have been in intensive use due to their environmental and technical merits. The identification of unknown parameters in photovoltaic (PV) models is one of the main issues in simulation and modeling of renewable energy sources. Due to the random behavior of weather, the change in output current from a PV model is nonlinear. In this regard, a new optimization algorithm called Runge–Kutta optimizer (RUN) is applied for estimating the parameters of three PV models. The RUN algorithm is applied for the R.T.C France solar cell, as a case study. Moreover, the root mean square error (RMSE) between the calculated and measured current is used as the objective function for identifying solar cell parameters. The proposed RUN algorithm is superior compared with the Hunger Games Search (HGS) algorithm, the Chameleon Swarm Algorithm (CSA), the Tunicate Swarm Algorithm (TSA), Harris Hawk’s Optimization (HHO), the Sine–Cosine Algorithm (SCA) and the Grey Wolf Optimization (GWO) algorithm. Three solar cell models—single diode, double diode and triple diode solar cell models (SDSCM, DDSCM and TDSCM)—are applied to check the performance of the RUN algorithm to extract the parameters. the best RMSE from the RUN algorithm is 0.00098624, 0.00098717 and 0.000989133 for SDSCM, DDSCM and TDSCM, respectively.