“…Metaheuristic methods, on the other hand, are algorithms inspired by nature to solve optimization problems for model parameter estimation. Artificial Bee Colony (ABC) algorithm is in [12], Rat swarm optimizer (RSO) in [13], adaptive differential evolution (DE) algorithm in [14], a performance-guided JAYA (PGJAYA) algorithm in [15], a cat swarm optimization (CSO) algorithm in [16], a flexible particle swarm optimization (FPSO) in [17], a modified simplified swarm optimization (MSSO) algorithm in [18], an improved chaotic whale optimization (CWO) algorithm in [19], an improved opposition-based whale optimization algorithm (WOA) in [20], an improved cuckoo search algorithm (ImCSA) in [21], an improved sine cosine algorithm (ISCA) in [22], an improved Lozi-map chaotic optimization algorithm in [23], an improved teaching-learning-based optimization (TLO) in [24], a coyote optimization algorithm (COA) in [25], a slime mould algorithm (SMA) in [26], a grey wolf optimizer (GWO) in [27] and [28], a sine cosine algorithm (SCA) in [29], an ant lion optimizer (ALO) in [30] and [31], an improved moth-flame optimization (MFO) algorithm in [32] and [33], an improved lion swarm optimization in [34], northern goshawk optimization algorithm (NGO) [10], a multiverse optimizer (MVO) in [35], modified whale optimization algorithm (MWOA) [36], an enhanced adaptive butterfly optimization algorithm (EABOA) in [37], and a manta ray foraging optimization (MRFO) in [38] have been applied for solving the PV parameter estimation.…”