2020
DOI: 10.1007/s12065-020-00499-1
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Parameter extraction of solar cell using intelligent grey wolf optimizer

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Cited by 28 publications
(18 citation statements)
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“…Similarly to the particle swarm Optimization algorithm, one of the new algorithms, which are based on the metaheuristic principle is the Grey Wolf Optimization GWO algorithm. The researcher, Mirjalili, was one of the first researchers who developed this algorithm and exposed its running principle in 2014 [32,43]. To obtain the optimum solution of the problem to be optimized, the algorithm principle uses social authority, which is represented by the behaviour of the wolves when surrounding a victim.…”
Section: B the Gwo Algorithmmentioning
confidence: 99%
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“…Similarly to the particle swarm Optimization algorithm, one of the new algorithms, which are based on the metaheuristic principle is the Grey Wolf Optimization GWO algorithm. The researcher, Mirjalili, was one of the first researchers who developed this algorithm and exposed its running principle in 2014 [32,43]. To obtain the optimum solution of the problem to be optimized, the algorithm principle uses social authority, which is represented by the behaviour of the wolves when surrounding a victim.…”
Section: B the Gwo Algorithmmentioning
confidence: 99%
“…The rest of the group is limited to take care of the injured wolves of the pack. When the prey stops moving, the wolves attack and finish the hunt [43].…”
Section: B the Gwo Algorithmmentioning
confidence: 99%
“…These structures are influenced by natural events, such as swarming activities, mechanisms focused on nature, and physics. Genetic algorithm (GA) [18], [19], particle swarm optimization [20]- [22], enhanced leader particle swarm optimization algorithm (PSO) [23], niche particle swarm optimization in parallel computing algorithm [24], several versions of differential evolution (DE) [25]- [28], penalty-based DE algorithm [29], sunflower optimizer [30], grey wolf optimizer (GWO) [31], whale optimizer algorithm (WOA) [32], harris-hawk optimizer (HHO) [33], improved salp swarm algorithm (ISSA) [34], several version of JAYA algorithm [35], multiple learning backtracking search algorithm [36], coyote optimization algorithm [37], teaching-learning-based optimization and its various versions [38]- [44], political optimizer (PO) [4], evolutionary shuffled frog leaping algorithm [45], slime-mould optimizer (SMO) [46], [47], marine predator algorithm (MPA) [48], equilibrium optimizer (EO) [49], ions motion optimization (IMO) [50], improved PSO (IPSO) [51], Forensic-based investigation algorithm [52], and improved learning-search algorithm [53] are among good heuristic-based structures. Some studies have endeavored to hybridize a few of these strategies to boost their performance, such as hybrid grey wolf optimizer with cuckoo search algorithm [54], hybrid firefly with pattern search algorithms [55], hybrid grey wolf optimizer with particle swarm algorithm [56], hybrid WO with DE algorithm [26], hybrid GA with simulated annealing algorithm [18], etc.…”
Section: Introductionmentioning
confidence: 99%
“…Grasshopper Optimization Algorithm was presented to estimate the PV parameters of TDM in [31]. An intelligent grey wolf optimizer was presented in [32], to estimate the PV panel parameters of SDM and DDM. The idea behind intelligent gray wolf optimizer is the incorporation of opposition based learning to the conventional gray wolf optimizer (GWO) to enhance the exploration and exploitation phases.…”
Section: Introductionmentioning
confidence: 99%