**5. Research Gap and Findings**

There are total 16 techniques reported in this paper. In 23 papers conventional MPPT techniques, 42 papers swarm intelligence MPPT techniques, 21 papers bio-inspired, and in 35 papers other AI-based techniques are discussed. Therefore, a total of 121 papers were mainly studied, which are focused on these MPPT techniques. The remaining 23 out of 144 papers were used in other important sections. The classification of papers focusing on different techniques can be seen in Figure 24.

**Figure 24.** Papers focused on different MPPT techniques.

The authors are mainly classified concerning conventional MPPT techniques, metaheuristic AI techniques, and other AI-based techniques. Further, conventional MPPT techniques are classified as perturb and observe, incremental conductance, fractional opencircuit voltage, and fractional short-circuit current; particle swarm optimization, artificial bee colony, grey wolf optimization, and salp swarm algorithm fall under swarm intelligence MPPT techniques; and firefly MPPT algorithm, cuckoo search, and flying squirrel search optimization techniques are classified as bio-inspired techniques [141–144]. While swarm intelligence and bio-inspired techniques are metaheuristic AI techniques, other AIbased MPPT techniques are fuzzy logic control, artificial neural network, and evolutionary computational techniques (genetic algorithm and differential evolution).

After conducting a thorough analysis of metaheuristic MPPT approaches based on conventional and AI techniques in this paper, one can easily find the following gaps in this area:

