## 4 Improved Eagle Strategy  
In this section, the proposed method with the combination of ES and K-means clustering technique is discussed first, followed by the presentation of the algorithm. We named the proposed method the Improved Eagle Strategy (IES) algorithm, and the overview is illustrated in Figure 8. The proposed method is an improved version of the ES method that may be able to obtain balance exploration and exploitation and may also outperform other existing bio-inspired methods. ES was developed by [PERSON] and [PERSON] [21]. This technique has been used by researchers to enhance the efficiency of metaheuristic algorithms. Figure 9 below overview of IES. We propose an improvement to the ES algorithm by applying the K-means clustering technique to cluster similar WS, since WS are growing in numbers, and this growth makes retrieval of the correct services upon user request difficult. To recognize the necessary WS, we propose to make it compulsory to cluster the WS to make the services easily discoverable. The WS cluster would arrange and manage the services to narrow down the scope of the search space. K-means is a common clustering technique used in text clustering and was introduced by [PERSON] and other researchers in 1967 [18]. The algorithm initially assigns a random initial cluster centroid. This method is also widely used and performs better for big datasets [12]. This algorithm can use the maximum similarity measure to assign each service for a centroid based on a similar cluster. The algorithm of the K-means clustering technique is presented in Algorithm 2. Figure 7: Activities carried out during the development phase. Figure 8: Steps to test the correctness of the selected service. Figure 9: Overview of Improved Eagle Strategy (IES). In this research, the K-means clustering technique was applied to the services to group the services within similar categories. Before applying the clustering technique, semantic similarities were measured by using WordNet 3.0, which is a free and publicly available software for download. Further, the structure of WordNet 3.0 makes it a useful tool for computational linguistics and natural language processing. Before calculating semantic similarities, the input was retrieved from WSDL and used to extract important data, such as the method name and input-output parameters. The method name was the functionality of the service; the input and output elements specify the abstract message format for the solicited request and response, respectively. Semantic similarities calculation is an important phase because the clustering of WS requires the calculation of distance between services. WS clustering consists of extracting characteristics of WS from the repository and discovering the similarities between the clusters or groups. Once the services have been clustered, the fitness value [22; 23] for each service was calculated using Equation (1) and classified as having a high or low fitness value by comparing each service with a threshold value [20]. Equation (2) is the formula used to calculate the threshold value of each service. Once both fitness value and the threshold value are identified, Equation (3) was used to identify if the service owns a high fitness value or low fitness value. The pseudocode of ES is presented in Algorithm 3. ```
1: Objective function f(x)
2: Initialization of sample space
3: While (t < maximumnumberofiterations)
4: Do Global Exploration by randomization
5: Fitness Evaluation and finding a promising solution
6: If (Pe < rand), do
7: Local exploitation by efficient local optimizer
8: If (a better solution is found)
9: current best solution is updated
10: End
11: End
12: t = t + 1;
13: END
```  
**Algorithm 2** K-means clustering [18].