## 5 Advanced MPPT Techniques
### Metaheuristic MPPT Techniques
#### 5.2.2 Bio-Inspired Algorithms
It operates by mimicking the biological processes of pollination and reproduction, exploring and exploiting the search space to find the MPP [90]. Advantages of the FP algorithm include its ability to handle complex, multi-dimensional optimization problems with fewer parameters to tune compared to some other algorithms. It is also robust against local optima due to its stochastic nature. Disadvantages include potentially higher computational complexity and convergence time compared to simpler algorithms, and it may require more careful tuning for specific applications to achieve optimal performance [91; 92; 93]. The following equations are used for the FP, where \({x_{i}}^{t}\) refers to the pollen, \({g_{best}}\) is the current best solution, \(\gamma\) is the scaling factor that controls the step size, and \(L(\lambda)\) represents the step size based on Levy flights. \(\Gamma(\lambda)\) is the gamma function, applicable for large steps. \({x_{i}}^{t}\) and \({x_{j}}^{t}\) are the pollens from different flowers of the same plant species, and \(\varepsilon\) denotes the local search distributed uniformly within the range [0, 1]. \[{x_{i}}^{t+1}={x_{i}^{t}}+\gamma L(\lambda)\big{(}{g_{best}}-{x_{i}^{t}}\big{)} \tag{20}\]  
\[L(\lambda)=\frac{\lambda\Gamma(\lambda)\text{sin}\big{(}\frac{\pi}{2}\big{)}} {\pi}-\frac{1}{S^{1+\lambda}}\,\ s\gg s_{0}>0 \tag{21}\]  
\[x_{i}^{(t+1)}=x_{i}^{t}+\alpha\oplus L\acute{e}vy(\lambda) \tag{15}\]0  
Genetic Algorithm (GA)-based MPPT algorithms are inspired by the principles of natural selection and evolution to optimize the power output of PV systems [94]. Figure 19 indicates the flowchart of the GA MPPT, where \(i\) is the generation number in the optimization process. The algorithm works by encoding potential solutions as chromosomes and applying genetic operations such as selection, crossover, and mutation to evolve over successive generations. The algorithm evaluates the performance of each solution based on a fitness function, which in this case is typically the power output of the PV system, and iteratively refines the solutions to approach the MPP. One of the main advantages of GA-based MPPT algorithms is their robustness and flexibility in exploring a wide range of possible solutions, making them effective for complex problems and environments with partial shading or rapid irradiance changes. However, GA can be computationally intensive and may require a considerable amount of time to converge to an optimal solution. Additionally, the performance of GA can be sensitive to the choice of parameters, such as mutation rates and population size, which may require careful tuning to achieve the best results [95; 96]. Table 5 provides a comparison of bio-inspired MPPT algorithms. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Tracking** & **Sensed** & **Circuity** & **Tracking Speed** & **Implementation Complexity** & **True MPPT** & **Accuracy** & **Cost** \\ \hline CSO & V, I & D & Fast & Low & Yes & High & High \\ FO & V, I & D & Very Fast & Medium & Yes & High & Medium \\ FP & V, I & D & Very Fast & Medium & Yes & High & Medium \\ GA & V, I & D & Fast & Complex & Yes & Medium & Medium \\ \hline \end{tabular}
\end{table} 
Table 5: Comparison of bio-inspired MPPT algorithms [97; 53]. Figure 18: Flowchart of the FP MPPT.