Improved moth flame optimization algorithm based on opposition-based learning and Lévy flight distribution for parameter estimation of solar module

An enhanced version of the moth flame optimization algorithm is proposed in this paper for rapid and precise parameter extraction solar cells. The OBLVMFO algorithm’s novelty lies primarily improved search strategies, where two modifications are to maintain a proper balance between exploration exploitation. Firstly, an opposition-based learning mechanism employed initialize population purpose enhancing global search. Secondly, Lévy flight distribution used prevent stagnation solutions local minima. implementation intelligent rules such as OBL significantly improves performance standard MFO. developed performed adequately reliable terms RMSE compared other methodologies MFO, ALO, SCA, MRFO, WOA. best optimized value achieved by 6.060E−04, 1.3600E−05, 7.0001E−06 STE 4/100 (polycrystalline), LSM 20 (monocrystalline), SS2018P (polycrystalline) PV modules, respectively. experiments on benchmark test function revealed that has 61% faster convergence speed than which solution accuracy. In addition this, non-parametric tests: Friedman ranking Wilcoxon rank sum validation.