## 3 Material and Methods
### Methodology
#### 3.2.2 Individual Random Search
## 5 Discussion

After simulating the case of cocoa flower pollination by using various random search algorithms, the main finding is that Levy Flight and DAG outperformed the selected SI search approaches, particularly Idle-Jaya, Idle-CSA, and Idle-Cuckoo Search (that also includes Levy flight). Levy flight and DAG could pollinate more trees than Idle-Jaya, Idle-CSA, and Idle-Cuckoo in the first several minutes of the simulation out of the three case simulations demonstrated. According to the paper [34], by comparing the search algorithm for neural architecture search (NAS), the evolutionary algorithm is better at handling optimization on NAS. Bn contrast, a random search may be faster but does not guarantee the best results in the case of the Pollination Problem. The DAG method used here is not that different from general evolutionary methods. It can be related to a particular case of Differential Evolution (DE). The original DE procedure is summarized as follows: A simple differential mutation operation resulting from two different individuals chosen from the population to disturb a randomly selected individual as the base vector. Then generate progeny candidates and a one-to-one selection strategy to determine which individuals are still surviving [35]. The main point is that DE adds the difference vector between two other individuals to itself or a third randomly selected position for a new search candidate position. DAG essentially does the same by adding the difference vector of Defender and Aggressor to the Defender. Thus, it is a differential algorithm as well. DE is as known as a strong metaheuristic in many application cases \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Experiment 1** & **Experiment 2** & **Experiment 3** \\ \hline Idle-Jaya & 9.55 & 12.10 & 15.50 \\ Idle-CSA & 10.10 & 13.20 & 13.95 \\ Idle-Cuckoo & 9.00 & 15.10 & 10.40 \\ Levy Flight & 10.50 & 18.80 & 15.50 \\ DAG & 11.70 & 17.70 & 16.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The average nectar amount from all simulations. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Experiment 1** & **Experiment 2** & **Experiment 3** \\ \hline Idle-Jaya & 0.53 & 0.47 & 0.63 \\ Idle-CSA & 0.47 & 0.39 & 0.55 \\ Idle-Cuckoo & 0.49 & 0.40 & 0.64 \\ Lévy Flight & 0.48 & 0.37 & 0.58 \\ DAG & 0.41 & 0.31 & 0.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The Gini Index average of the simulated representation of FP distribution nectar. while being easy to use and implement. Based on the results, we can conclude that DAG outperforms other Idle-SI algorithms. DE is also a favoured algorithm for solving real-valued continuous optimization problems [36; 37; 38]. In addition to DAG, Levy flight produces good results in second place after DAG. This result also supports the author's [39] proposal to include Levy flight in the Jaya basic algorithm to improve exploration and exploitation capabilities during the search process. In this paper, Levy flight is proposed to be incorporated into Jaya basic algorithm to facilitate the global search in the initial stages and local stages of the last investigation, increasing the algorithm's exploration and local optima avoidance capabilities. However, Idle-Cuckoo search, a derived SI algorithm from Cuckoo Search, already includes a Levy flight and the results show the worst performance among all algorithms studied here. What is the main benefit of such studies? After all, we cannot consider a head-to-head competition among algorithms as is common in other studies on the application of meta-heuristic algorithms, for example [40]. This is simply due to the lack of an immediately available fitness function. However, even when reducing such algorithms to their exploration component, it shows significant differences in the congruent goal of pollinating trees that are concomitant to nectar collection.