**Phase 1: Initialization phase**

First, create Ns food sources at random in the hunt arena. The algorithm's performance improves with the increase in size of the group. Each solution Yi is an n-dimensional vector that dispenses the entire employed bee equivalent to each distinctive source of food as per Equation (18) with n optimization parameter numbered as

$$Y\_{i,k} = Y\_{\min,i} + rand[0, 1](Y\_{\max,i} - Y\_{\min,i})\tag{18}$$
 
$$i = 1, 2, 3, \dots, \dots, N\_{\\$} \& \ k = 1, 2, 3, \dots, \dots, n$$

### **Phase 2: Employed bee phase**

The goal is to chase the food source location in the exploration region with the most nectar accessible (i.e., GMPP). Every employed bee progresses to its new position (Xi, k) in the immediate space by means of the previous position value (Yi) to maintain the previous position value (Yi) securely in memory according to Equation (19):

$$X\_{i,k} = Y\_{i,k} + a\_{i,k} \left( Y\_{i,k} - Y\_{j,k} \right); \ j = 1, 2, 3, \dots, \dots \text{ N}\_{\sf s} \tag{19}$$

Yj is other than Yi, i.e., i = j, and αi,k ranges from [−1, 1].

A gluttonous assortment method is adopted by employed bees after they search a new food source. The quantity of nectar present at the previous and latest sites is compared in this technique. As a result, a better option is preserved.

#### **Phase 3: Spectator bee phase**

On the basis of the information of the food source obtained by spectator bees from employed bees with their shake dance, spectator bees use a probabilistic selection mechanism in order to identify food sources (solutions) with f(x) fitness factor according to Equation (20).

$$\mathfrak{p}\_{i} = \frac{f(\mathbf{x}\_{i})}{\sum\_{n=1}^{N\_{s}} f(\mathbf{x}\_{i})}; i = 1, 2, 3, \dots, \dots, N\_{s} \tag{20}$$

#### **Phase 4: Scout bee phase**

Scout bees can locate fresh feasible solutions on the basis of Equation (20) in the vicinity of the chosen food source. In any event, even after a thorough investigation of the entire investigated area by employed and spectator bees, the food-source fitness value remains unaffected for the existing step. The same employed bees turn into scout bees, and the scout bees use Equation (18) to hunt for new possible solutions in the next step.

#### **Phase 5: Conclusion phase**

In case that output power does not show any further improvement, the method comes to an end. The procedure, on the other hand, will restart when there is a fluctuation in output power on account of various factors. Irradiance variation is one amongst them, and such changes can be represented as

$$\left|\frac{P\_{pv} - P\_{pv\,\,old}}{P\_{pv\,\,old}}\right| \ge \Delta P\_{pv} \% \tag{21}$$

If Equation (21) is satisfied, ABC again starts searching GMPP. Hence, ABC works well in PSCs. Figure 14 shows a flowchart of the ABC technique.

**Figure 14.** ABC-based MPPT technique [50].
