## 1 Introduction
### Literature
LFF generate sharp large fluctuations that equate the voltage surges in the DC Boost converters. The overall distribution (OD) algorithm in [15] is fused with PSO for rapid convergence time. The adaptive cuckoo optimization algorithm (ACOA) [16] minimizes the LFF impact in an iterative manner to avoid the voltage surges and increase the convergence time. The farmland fertility optimization (FFO) [17], spline-guided Jaya (SGJ) algorithm [18], sale swarm optimization algorithm (SSA) [19], and Butterfly optimization algorithm (BOA) [20] work in a similar manner using dynamic variables to control the balance between the exploitation and exploration phases of the GMPP-searching mechanisms. The artificial bee colony (ABC) [21] used dynamic association of searching members and scout members to achieve the GMPP and avoid the LM traps. To minimize the impact of random vectors in a standard PSO, an improvised one is modeled using the particle swarm optimization gravitational search (PSOGS). In these algorithms, the population size, information exchange mechanisms, number of epochs and iterations, convergence rates, parametric tuning, and computation burden are just a few examples of the variables that affect the SI performance [22]. Another recent study that used GAs for MPPT in TEG systems found that the GA algorithm was able to improve the performance of the TEG system, compared to other MPPT techniques, such as P&O and IC. Figure 4: P–V curve of TEG modules under (**a**) uniform temperature and (**b**) non-uniform temperature. A random number will produce unexpected oscillation and affect the stability time of the particle swarm optimization algorithm [18]. PSOGS combines the effective exploration of PSO with the precise development of GS, which greatly improves the work efficiency of PSO. Even though the steady-state oscillation has improved, the stabilization time has not been greatly reduced. The number of parameters that must be adjusted, the level of technical difficulty, and the computational load all rise as a result of the combination of GS and PSO. By using the arithmetic optimization method (AOA) [23], the random and adaptive parameter selection principle greatly boosts the AOA convergence performance. ACO uses pheromones to share information. Pheromones evaporate over time. Each ant is a member of a collection of possible solutions. Scout ants are also used to explore the search space [24]. Due to the selection of candidate solutions using probability functions, the undesired solution is selected for the output. The fitness-dependent algorithm (FDO) [25] and ABC mimic bee behavior. The pheromone evaporation rate yields solution degradation causing defective final solutions. Similarly, in CSA, Levy flight is used to allocate burst random values, which will lead to large fluctuations in the output transients [13]. The study found that the GA algorithm was able to track the maximum power point more accurately, leading to a higher power output and better energy harvesting. In a small size population, shortcomings such as oscillations and fluctuations predominate [26]. To smoothen the oscillations and enhance stability, a larger number of search agents is recommended. Consequently, the computing power is affected by exponential and convergence time delays. The main objectives required from the MPPT techniques are GM detection, robustness, fast convergence, least random oscillation, and high power convergence [27]. Thus, the maximum available energy is delivered to the load. Although GHO, CSA, ACO, etc., exhibit a reasonable power tracking ability, the logical flow of these algorithms contains randomness and is more complicated than the traditional PSO algorithm, which causes their actual implementation to be challenging in low-cost MPPT control [28; 29].