Artificial bee colony algorithm based on a new local search approach for parameter estimation of photovoltaic systems

In this study, an ABC-Local Search (ABC-Ls) method was proposed by including a new local search procedure into the standard artificial bee colony (ABC) algorithm to perform parameter estimation of photovoltaic systems (PV). The aim ABC-Ls improve exploration capability ABC with in addition exploitation and balance algorithm. first tested on 15 well-known benchmark functions literature. results Friedman Mean Rank test used for statistical analysis, successfully ranked value 1.300 functions. After obtaining successful tests, applied single diode, double diode Photowatt-PWP-201 PV modules estimations. addition, has been KC200GT module under different temperature irradiance conditions modules. success compared genetic (GA), particle swarm optimization (PSO) algorithm, tree seed (TSA), Jaya, Atom (ASO). comparison were presented tables graphics detail. RMSE values ABC-LS found as 9.8602E−04, 9.8257E−04 2.4251E−03, respectively. context, literature it that provides similar or better than other studies. is shown convergence graphs box plots, where achieves more successful, effective stable GA, PSO, ABC, TSA, Jaya ASO algorithms.