*3.2. Di*ff*erential Evolution (DE)*

This algorithm was suggested by Price and Storn in 1995. DE is a randomly varying population-based algorithm and it finds its application in global optimization problems [36]. This algorithm is well-suited for non-linear, non-differentiable, multi-dimensional problems [37]. Therefore, this algorithm can be implemented for PV panel maximum power extraction as the PV characteristics possesses a highly non-linear graph as they are intermittent in nature [16,20]. Furthermore, even during partial shading conditions, it can track the global optimum power point [38,39]. In the DE algorithm, the complexity reduces as it requires much fewer parameters (particles) to tune. This tuning of particles makes sure that in every iteration, the particles converge toward the best solution in the search space. DE algorithm follows various steps for optimization and those are initialization, mutation, recombination/crossover, and selection [40]. The DE algorithm flowchart is shown in Figure 6.

**Figure 6.** DE algorithm.
