A biogeography-based optimization algorithm with mutation strategies for model parameter estimation of solar and fuel cells

Modeling of photovoltaic system for uniform and non-uniform irradiance: A critical review

“…Other evolutionary algorithms for extraction of parameters are bird mating algorithm [100], simplified BMA [101] which reduces the parameter setting in original BMA, artificial bee swarm optimization [102,103], frog leaping algorithm [104], harmony search method [105], fish swarm optimization [106], TLBO [107], simplified TLBO [108], biogeography based optimization [109] ( Table 6). …”

Generation and combination of the solar cells: A current model review

“…18,19,[21][22][23] There are two different types of models for a PV cell. 22,26,27 On the other hand, it seems that there are some other effective factors other than the above-mentioned ones including the photocurrent generated in Standard Test Conditions (STC) I ph , the dark (or leakage) saturation current I 0 , and the cell temperature T. 18,[28][29][30] Also, because of using an electrical model, two other parameters will be used in the models including the unit of electrical charge q (1.6 × 10 −19 C) and the Boltzmann constant κ (1.38 × 10 −23 J/K) and it seems that the number of extracted key parameters of the models can change to improve the accuracy in different generations. This sort of model investigates some physical concepts such as the distribution of charges, efficient depth of the cell, and few others.…”

Optimal proton exchange membrane fuel cell modelling based on hybrid Teaching Learning Based Optimization – Differential Evolution algorithm

“…To conquer this drawback, metaheuristic algorithms such as Genetic Algorithms (GA) [20][21][22][23][24][25], Simulated Annealing (SA) [26,27], Differential Evolution (DE) [28,29], Particle Swarm Optimization (PSO) [30,31], Artificial Immune System (AIS) [5], Seeker Optimization Algorithm (SOA) [32], Harmony Search (HS) [33,34], Hybrid Artificial Bee Colony (HABC) [19], Artificial Bee Swarm Algorithm (ABSA) [35], P System Based Optimization (PSBO) [36], Teaching-learning-based optimization (TLBO) [37], Biogeography-based optimization [38] and Bird Mating Optimization (BMO) [39] have been applied in this problem. Metaheuristics generally do not need domain information and they are derivative free methods which perform stochastic movements to obtain global optimum point.…”