## 1 Introduction
### Literature  
In the literature, several aspects of TEG systems have been studied. Smart Maximum Power Point Tracking (MPPT) is essential for optimizing the performance of renewable energy systems thermoelectric (TEG). Using TEG systems as a heat recovery unit with smart MPPT control is shown in Figure 1. The TEG modules are exposed to the hot surface, and the temperature differential across the TEG module, using the Peltier's and Seebeck's effect, generates the voltage potential that is modulated using the DC boost converter via the PWM signal generated by the MPPT controller under conditions provided by the current and voltage sensors [1]. Figure 2 shows the internal structure and electrical equivalent model. Figure 3 shows one of the most cost-effective topologies of TEG system to act as a heat recovery system. The electrical characteristics exhibited in Figure 4 show that the control problem in hand is not only nonlinear but also mathematically monotonic and multiple maxima [2]. MPPT is used to track the maximum power point of the TEG and to ensure that the TEG operates at its maximum power output. This is important in TEG systems that are used for heat recovery applications, where the TEG converts waste heat into electrical energy. The classical MPPT techniques that have been developed for TEG systems include perturb and observe (P&O), incremental conductance (IC) in fusion with fuzzy logic control (FLC), machine learning (ML) models, and deep neural networks (DNN). Unlike PV and wind energy conversion systems, these techniques are less explored in TEG systems. The P&O technique is a simple and widely used MPPT technique that adjusts the TEG's operating voltage in fixed magnitude in forward or reverse depending upon the change in output power. Since the decisions are made due to the gradient ascend or descend in the presence multiple local peak solution (shown in Figure 4), the MPPT control, hence, may trap in local maxima, thus losing significant available power. The IC technique is similar to the P&O technique, but it uses a conductance parameter, which is a more sophisticated control algorithm to adjust the TEG's operating voltage. Figure 1: TEG system with MPPT. Figure 3: Centralized TEG configuration with load and MPPT converter. Figure 2: TEG module structure with equivalent electrical circuit. In recent years, there has been a growing interest in the development of novel MPPT techniques for TEG systems. These optimization algorithms have been shown to be effective in improving the performance of TEG systems by providing a more accurate and efficient method for tracking the maximum power point. The study found that the PSO algorithm was able to track the maximum power point more accurately and quickly, leading to a higher power output and better energy harvesting. MPPT may be thought of as an optimization issue. Swarm-intelligence-based optimization techniques and other meta-heuristic evolutionary algorithms have a great deal of potential to overcome the drawbacks of FLC, ML, and traditional MPPT methods [3]. The FLC technique uses fuzzy logic to determine the TEG's operating voltage, which is then adjusted to track the maximum power point. The nonlinear multiplicity of the base system's less precise equivalent modeling can be handled by the SI with ease. For PSO and for MPPT in TEG systems, [4] found that the PSO algorithm was able to significantly improve the efficiency of the TEG system, compared to other MPPT techniques, such as P&O. In this class, particle swarm optimization (PSO) [5; 6], grey wolf optimization (GWO) [7], Harris hawk optimization (HHO) [8], grasshopper optimization (GHO) [9], and ant colony optimization (ACO) [10; 11] are most implemented techniques. The simulated annealing (SA) performance in [12] is assessed by considering the converge time and the number of samples to converges to the GMPP. A cuckoo search (CS) algorithm has variants that generate the searching pattern using the Levy Flight Functions (LFF) as in [13; 14].