Maximum power point (MPP) technique in photovoltaic (PV) systems implements a tracking controller, which is utilized to optimize the energy production under variable atmospheric conditions. The tracking process becomes more difficult due to appearance of many peaks owing to partial shading conditions. Although conventional and soft computing technologies are frequently used to solve MPP tracking issues, their performance is constrained by the fixed step size of conventional methods. However, once soft computing methods reach a certain MPP, they are constrained by a lack of randomness. The novel adaptive flower pollination algorithm (AFPA) optimization technique proposed in this work, proceeds with global and local searching in a single step, which is very crucial for the success of the MPP tracking with this method. The robustness of the approach is examined by conducting zero, weak, moderate, and strong shading patterns to a complete performance assessment via simulation, and that performance is compared with traditional flower pollination algorithm (FPA) and particle swarm optimization (PSO) techniques. This newly proposed method has the following advantages over the conventional FPA: a) risk of failure is zero; b) oscillation of power, voltage, and current across the load is minimized; c) produced energy is increased by 0.5 to 2.5% with respect to FPA; d) MPP is tracked smoothly and e) reduced MPP tracking time by average 45%. This advantage is especially noticeable in the dynamic variation of the shading patterns.

光伏 (PV) 系统中的最大功率点 (MPP) 技术采用跟踪控制器，用于优化可变大气条件下的能源生产。由于部分阴影条件导致出现许多峰值，跟踪过程变得更加困难。尽管传统和软计算技术经常用于解决 MPP 跟踪问题，但其性能受到传统方法固定步长的限制。然而，一旦软计算方法达到一定的 MPP，它们就会受到缺乏随机性的限制。这项工作中提出的新型自适应花卉授粉算法（AFPA）优化技术，一步进行全局和局部搜索，这对于使用该方法进行 MPP 跟踪的成功至关重要。通过模拟对完整的性能评估进行零、弱、中等和强着色模式来检查该方法的鲁棒性，并将该性能与传统的花授粉算法（FPA）和粒子群优化（PSO）技术进行比较。这种新提出的方法与传统的 FPA 相比具有以下优点： a) 故障风险为零； b) 负载上的功率、电压和电流振荡最小化； c) 产生的能量相对于 FPA 增加 0.5% 至 2.5%； d) MPP 跟踪顺利，e) MPP 跟踪时间平均减少 45%。这一优点在阴影图案的动态变化中尤其明显。