## 2 Methods
### Symbiotic Organisms Search-Least Square Support Vector Machine
#### 2.2.2 Symbiotic Organisms Search (SOS)  
Symbiotic organisms search is a new metaheuristic algorithm introduced by [31]. Symbiotic organisms search is a new metaheuristic algorithm introduced by various researchers [33; 34]. This is a symbiotic interactive technique to move the population of the solution, i.e., the ecosystem of the organism, to better and promising areas of search space while searching for an optimal global solution, and this is done iteratively. Organisms in the ecosystem have unique fitness values, which implies their degree of adaptability to the object that is desired or demanded. The steps involved in the SOS algorithm are as follows:  
* Generation of the initial population
* Do
* Mutualism
* Commensalism
* Parasitism
* Best solution update
* Until the criteria of stopping are satisfied.  
Researchers have studied other metaheuristic techniques such as Genetic Algorithm, Particle Swarm Optimization, Differential Evolution, etc., along with the SOS, and they concluded that SOS performs better than other techniques. It is more effective and efficient and produces better results. In addition, SOS has been inculcated in many research fields to solve engineering and other fields' problems [15; 35; 36].  
SOS-LSSVM as a hybrid AI technology that helps in creating a symbiotic environment for input and output. LSSVM acts as a supervised learning-based predictor. SOS optimizes the parameters used in the LSSVM algorithm, i.e., \(\gamma\) and \(\sigma\).  
The SOS-LSSVM technique is completed in eight significant steps categorized into phases: beginning and training phase followed by testing phase [31].  
1. Data are collected for training the model.
2. The LSSVM model is used to analyze the ambiguous nature of input and output. In addition, \(\sigma\) and \(\gamma\) are tuned.
3. SOS algorithm: This algorithm searches for several combinations of \(\sigma\) and \(\gamma\) parameters and makes the best set of these two parameters. In addition, SOS employs mutualism, commensalism, and parasitism phases to improve the fitness value of the solutions reached slowly.
4. Evaluation of fitness: For evaluation of the system, a fitness function is developed that measures the accuracy of the learning system. The best combination of \(\gamma\) and \(\sigma\) represents the accurate and best fitness value. The dataset is not split randomly, and it is are divided into learning and validation subsets. In addition, to avoid the bias of sampling, 10-fold cross-validation is done. The mean square error (MSE) is utilized by the fitness function for aptness and better representation.
5. Criteria of termination: The termination criterion used in this technique is the iteration number inculcated in the SOS algorithm.
6. Optimal \(\sigma\) and \(\gamma\) parameters: Loop stops and optimal \(\sigma\) and \(\gamma\) parameters are reached.
7. The optimal set of \(\sigma\) and \(\gamma\) parameters are further used for developing the model for testing the data.
8. Data testing: The dataset split for testing is tested, and the prediction is used for assessing the performance and accuracy of the model.