# K-RBBSO Algorithm: A Result-Based Stochastic Search Algorithm in Big Data
## 4 Discussion
Moreover, the experiments confirmed the smooth operation of K-RBBSO by merely configuring the number of colonies K, data-related parameters, and the number of habitats without the need to modify any other parameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Basis** & & **Advantage** & **Disadvantage** \\ \hline \multirow{6}{*}{K-RBBSO} & \multirow{6}{*}{K-means for initial solution setting and silhouette coefficient-based BBO algorithm} & - & Silhouette coefficient-based results & \multirow{6}{*}{-} & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \end{tabular}
\end{table} 
Table 4: Advantages and disadvantages of each algorithm. Moreover, the efficient operation of K-RBBSO based on a large number of data was demonstrated. When the volume of data was increased, the SA and ABC algorithms required much longer durations to yield results and identify better solutions. However, K-RBBSO exhibited a lower search time than the regular RBBSO and yielded satisfactory solutions. As presented in Table 3, the average execution time of K-RBBSO over 10 iterations was 2096.927 s, which was nearly 900 s quicker than the 2932.954 s of standard RBBSO. The highest execution time of K-RBBSO was 3036.95 s, compared to 5234.389 s for RBBSO. This is attributed to the poor quality of the initial solution established via random execution in the general RBBSO algorithm. Moreover, the evaluation values obtained using K-RBBSO and conventional RBBSO were not significantly different. The difference between the silhouette coefficients of K-RBBSO and RBBSO was evaluated (Table 2). The two algorithms exhibited no significant differences corresponding to the Iris and Wine data, but in the cases of Glass, Vowel, Cloud, and CMC data, K-RBBSO yielded somewhat superior evaluation values. This is attributed to the superiority of the determination of the initial solution using K-means--the BBO algorithm relies on the idea that \"better solutions produce better habitats\"; therefore, the quality of the initial solution is directly correlated to the likelihood of obtaining a better evaluation value. However, some issues must be considered when using the K-RBBSO algorithm. K-RBBSO is suitable for big data. In this study, the maximum data size was 1473. Therefore, the K-RBBSO algorithm was used. This study verified the effectiveness of the BBO algorithm. According to [PERSON] et al. [9], the BBO algorithm is the most powerful algorithm among nature-modeled algorithms. [PERSON] et al. conducted a review of the papers related to nature-modeled algorithms such as ABC, BBO, differential evolution (DE), fireflies algorithm (FA), genetic algorithm (GA), group search optimization (GSO), particle swarm optimization (PSO), and shuffled frog leaping algorithm (SFLA). Their study revealed that BBO, DE, FA, and SFLA are the fastest algorithms to solve optimization problems. However, ABC, GA, GSO, and PSO are too slow. The reason for the improved speed of the BBO algorithm is immigration, emigration, and mutation habitat problem-solving. Algorithms using immigration, emigration, and mutation have several problems, one of which is sensitivity. In the previous studies, other optimization algorithms were applied to solve the sensitivity of the BBO formulation problem. Thus, the improved BBO algorithm is more sensitive than the previous BBO algorithm and the other nature-modeled algorithm. The BBO algorithm provides a rationale to reduce the random function dependence of each process so that better results can be gathered. However, other algorithms have not been solved to reduce random function dependence.