Abstract
Summary
The Search and Rescue optimization algorithm (SAR) is a recent metaheuristic inspired by the explorations behavior of humans during search and rescue operations. Similar to many of the metaheuristic algorithms (MAs), SAR may suffer from drawbacks, such as being trapped in minimum local regions and improper balance between exploitation and exploration phases. To overcome these weaknesses, this study proposes an alternative algorithm of Search And Rescue optimization algorithm (SAR) called (mSAR) to improve its diversity and provide a soft balance between the exploration and exploitation stages of the original SAR algorithm. The mSAR introduces an adaptive strategy to boost the algorithm performance. To assess its reliability, the proposed approach is validated through IEEE CEC'2020 test suite against six state‐of‐the‐art algorithms namely the Adaptive guided differential evolution algorithm (AGDE), Evolution strategy with covariance matrix adaptation (CMA‐ES), the Whale optimization algorithm (WOA), Harris hawks optimization (HHO), Archimedes optimization algorithm (AOA), Cuckoo search algorithm (CS) besides the original SAR algorithm. A robust strategy based on a mSAR to create an equivalent circuit for a high‐efficiency triple‐junction solar cell/module (TJS/M) has been proposed. The suggested strategy is used to determine the best parameters of the TJS/M model based on the measurement datasets. During the optimization process, the aim is to minimize the integral time absolute error (ITAE) between the measured and estimated currents. The suggested mSAR is compared with other optimizers considering statistical tests of Wilcoxon sign rank, Friedman, and ANOVA. For the case of TJSC based PV module, the best (minimum) objective function is achieved at a value of 0.00178 by the proposed mSAR, whereas the worst value at 0.02476 is obtained by the CS. Furthermore, for the single TJSC, the minimum objective function is achieved at value of 0.04277 by the proposed mSAR whereas the worst value at 0.19987 is obtained by the CS. The best average cost value at 0.00663 is achieved by the proposed mSAR, whereas the worst value at 0.0402 is obtained by the HHO. The best SD value at 0.0061 is achieved by the proposed mSAR, whereas the worst value at 0.03845 is obtained by the AOA. The obtained results proved the superiority of the proposed mSAR in determining the best parameters of the TJS model.