In the case of partial shading conditions, there will be more than one maximum power point (MPP) in photovoltaic (PV) array. The traditional maximum power point tracking (MPPT) methods are easy to get in the local maximum power point (LMPP) and fail. Based on the standard differential evolution (DE) algorithm, the mutation strategy, scaling factor F, and cross factor CR of the algorithm are optimized. Also, the population position variance δ 2 is used to prevent falling into LMPP. Finally, the conditions for algorithm termination and restart are set. It is verified by simulation that the method has fast convergence speed, high accuracy, and can adapt well to changes in the external environment. The improved DE algorithm has a great advantage in MPPT.Energies 2020, 13, 1254 2 of 15 model and has strong robustness. However, it is difficult to establish a fuzzy rule because it needs to accumulate a lot of operation experience of power point tracking. In references [11,12], neural networks are used to track the MPP. This method also requires the distribution of power extremum points under many different shadows, so it is not practical. In references [13,14], Fibonacci is used to track the GMPP; this method also depends on the learning to the peak distribution, and it is easy to fall into LMPP. Reference [15] proposed a dichotomy, the first step is to find the vicinity of GMPP, and the second step is to use traditional MPPT method to track MPPs, but the dichotomy will fail in the case of complex shading. Reference [16] proposed a MPPT method using shading detection and the trend of slopes from each section of the curve. After the short-term and long-term testing, this method shows the advantages of high precision and short tracking time. In reference [17] a modified P&O MPPT algorithm is proposed for accurate detection of PSC, which stabilizes the system output voltage without compromising the power efficiency. Reference [18] proposed an algorithm that is based on bio-inspired Whale Optimization. This algorithm eliminates the computational burden faced by the hybrid MPPT algorithms as discussed in various references and reduces the power oscillation during the change in operating conditions. Reference [19] proposed a modified P&O MPPT that can be used under PSC effectively, by integrating the Artificial Bee Colony algorithm in the first stage and P&O algorithm in the second stage. However, the algorithm needs many parameters to be adjusted, which is not conducive to control.Based on the advantages and disadvantages of the above methods, the DE algorithm is used to optimize the output of PV array. DE only needs two adjustable parameters. Firstly, according to the method proposed in reference [20], the initial positions of particles are selected near the voltages corresponding to the possible MPPs to avoid falling into the LMPP. The mutation direction, mutation operator, and crossover operator of particles are optimized based on the traditional DE algorithm.