*4.3. Bio Inspired Techniques*

This part of the paper elaborates various MPPT techniques inspired by biological behavior of different organism. Additionally, various recent works done to track MPP incorporating these techniques are tabulated in Tables 5 and 6.

#### 4.3.1. Firefly MPPT Algorithm

Fireflies are beetles emitting light in the night and communicate amongst themselves using a special light pattern. The light color formed by each species is unique. The FFA's hunting tactic is governed by firefly attraction, which is equivalent to brightness. A dimmer firefly approaches a brighter one, and if their brightness level is the same as that of a certain firefly, it will shift at random [86]. The key purpose of flashing in the FFA tactic is to allure other fireflies and attract their target. The charm of fireflies is governed by the intensity of the firefly along with the objective function value. The value of attraction "μ" is resolute by the evaluation of other fireflies and is diverge on the basis of "i" and "j" fireflies' distance "Dij". Both can be evaluated as per Equations (30) and (31), with "D" as the distance between two fireflies, "β" as an arbitrary constant that lies between 0.1 and 10, and "n" as the dimension number.

$$
\mu = \mu\_0 \ e^{-\beta D^2} \tag{30}
$$

$$D\_{ij} = |\mathbf{x}\_i - \mathbf{x}\_j| = \sqrt[2]{\sum\_{y=1}^n (\mathbf{x}\_{i,y} - \mathbf{x}\_{j,y})^2} \tag{31}$$

D = 1 is taken in MPPT problems because it is a one-dimensional case. A flowchart of FFA is shown in Figure 18.

**Figure 18.** FFA-based MPPT technique [46].

#### 4.3.2. Cuckoo Search

This bio-inspired technique was reported in 2009 and is inspired by the cuckoo species' parasitic imitation tactic (brood-parasitism) [87]. Certain birds, such as cuckoos (Tapera), engage in social parasitism. The Tapera is a knowledgeable winged creature that fits in with the host fowls, and with this tactic, next-generation endurance is encouraged. Rather than building its own nest, the cuckoo places its eggs in the nests of other flying species. Primarily, the cuckoo bird (female) flies erratically in search of a nest with similar egg characteristics to their own. After finding the best nest, cuckoo eggs have the utmost opportunity of hatching, ensuring the new generation. The cuckoo makes a few attempts

by assisting the incubating bird in laying their eggs in a suitable location and hence gives itself a better chance. The cuckoo may occasionally throw the eggs of the host species from the nest because host birds could be readily duped into recognizing the strange eggs. If the host bird comes to know about the foreign eggs, the eggs will definitely be dumped outside the nest. The host bird may even demolish the nest.

For optimization objectives, the CS approach is an effective meta-heuristic method. Three idealized principles used to accomplish this strategy are as follows:


Cuckoo birds represent the particles relegated to find the solution in the CS strategy implementation, and their eggs indicate the current iteration's solution to an optimization problem. Searching for a nest is comparable to searching for food, and in CS, it is described by Levy flight. A Levy flight "y" is an arbitrary stride where Levy distribution is used to evaluate sizes of steps by using a power law [88]:

$$y = L^{-\gamma} \; ; \; (1 < \gamma < 3) \tag{32}$$

Thus, "y" has an infinite variance. The new cuckoo solution xi+<sup>1</sup> for ith iteration cycle "i" and the nth particle "n" can be generated as

$$\mathbf{x}\_{n}^{i+1} = \mathbf{x}\_{n}^{i} + z(\text{ }levy\ (\gamma))\tag{33}$$

"z" is a mathematical operator that represents the multidimensional problem's entrywise multiplication.

In each iteration cycle, all particles transmit Levy flights until they find GMPP. Figure 19 shows the flowchart of the CS algorithm to track GMPP.

**Figure 19.** CS-based MPPT technique [87].

### 4.3.3. Flying Squirrel Search Optimization

This bio-inspired optimization approach to track GMPP was introduced in 2020 and mimics the highly effective hunting tactic used by southern flying squirrels [89]. This approach also mimics the squirrels' manner of buoyant headways in the air. The posture of FS is referenced to as the feasible outcome vector and the comparable wellness is typical food source, respectively.

The posture is divided into three districts addressing sets based on wellness value:


Following assumptions are made while incorporating FSSO [89] in tracing GMPP: The food supply point is similar to the power yield from PV;

DC converter duty ratio (*∂*) in the MPPT approach is regarded as option variable, i.e., the posture;

To reduce the tracking time, the FSSO approach is custom-fitted by eliminating the occurrence of hunters.

The following steps are taken into account while implementing the FSSO technique.

Starting: Initially, FSs "N" numbers are placed at various locations. In the solution area, the duty ratio of the DC converter can be estimated for "i" iteration count by these points as follows:

$$
\partial\_i = \partial\_{\min} + \frac{(i - 1)(\partial\_{\max} - \partial\_{\min})}{N} \ : i = 1, 2, 3, \dots, N \tag{34}
$$

Wellness evaluation: The DC converter employed is gradually running with each duty ratio in this progression (i.e., with each FS posture). Each food source feature shows instantaneous power yield PV (*∂*) for each "*∂*". This sequence is repeated for all "*∂*", whereas MPPT goal wellness function " *f*(*∂*)" can be determined as

$$f(\partial) = \max\left(PV(\partial)\right) \tag{35}$$


Important conditions followed in FSSA are as follows:

Occasional observing conditions: These conditions help FSSA to avoid being stuck in LMPP. The cyclic constant (OC) and its base value (Omin) for a single-dimensional space with "i and im" as the count of the present and maximum number of cycles allowed are

$$O\_{\mathbb{C}}^{i} = \left| \mathfrak{x}\_{at}^{i} - \mathfrak{x}\_{\text{ht}} \right| \tag{36}$$

$$O\_{\rm min} = 10^{-6} / \text{\textsuperscript{\ddagger}{\\$}65^{\dagger\_{\perp/6}}} \tag{37}$$

For investigating the superior search area, Levy distribution is employed. As a result, the OTFS duty cycle is relocated.

• Groove contemporized: Squirrels of hickory tree maintain their position. The squirrels on acorn tree, on the other hand, find a way to access the hickory tree. The arbitrarily chosen squirrel (ATFS) from normal trees chooses the hickory tree, while the leftover (NTFS-ATFS) is pressed to the acorn tree. The duty cycle is changed:

$$
\partial\_{at}^{i+1} = \partial\_{at}^i + H\_c h\_d \left( \partial\_{ht}^i - \partial\_{at}^i \right) \tag{38}
$$

$$
\partial\_{ot}^{i+1} = \partial\_{ot}^i + H\_c h\_d \left(\partial\_{ht}^i - \partial\_{ot}^i\right) \tag{39}
$$

$$
\partial\_{ot}^{i+1} = \partial\_{ot}^i + H\_c h\_d \left(\partial\_{at}^i - \partial\_{ot}^i\right) \tag{40}
$$


$$\frac{P\_{p\upsilon}^{i+1} - P\_{p\upsilon}^i}{P\_{p\upsilon}^{i+1}} \ge \Delta P \text{ (\%)}\tag{41}$$

The complete steps of FSSO algorithm in tracking GMPP are depicted in Figure 20.

**Figure 20.** FSSO-based MPPT technique [89].




**Table 6.** Pros and cons of recent work based on bio-inspired techniques.
