  The increasing use of fossil fuels to produce energy is leading to
environmental problems. Hence, it has led the human society to move towards the
use of renewable energies, including solar energy. In recent years, one of the
most popular methods to gain energy is using photovoltaic arrays to produce
solar energy. Skyscrapers and different weather conditions cause shadings on
these PV arrays, which leads to less power generation. Various methods such as
TCT and Sudoku patterns have been proposed to improve power generation for
partial shading PV arrays, but these methods have some problems such as not
generating maximum power and being designed for a specific dimension of PV
arrays. Therefore, we proposed a metaheuristic algorithm-based approach to
extract maximum possible power in the shortest possible time. In this paper,
five algorithms which have proper results in most of the searching problems are
chosen from different groups of metaheuristic algorithms. Also, four different
standard shading patterns are used for more realistic analysis. Results show
that the proposed method achieves better results in maximum power generation
compared to TCT arrangement (18.53%) and Sudoku arrangement (4.93%). Also, the
results show that GWO is the fastest metaheuristic algorithm to reach maximum
output power in PV arrays under partial shading condition. Thus, the authors
believe that by using metaheuristic algorithms, an efficient, reliable, and
fast solution is reached to solve partial shading PV arrays problem

Neural and Evolutionary Computing