A Web Service Selection Model Using Grey Wolf Optimization with BWM and TOPSIS
Hongda Wang, Zhichun Jia, Bo Shao, Yiwen Wang, Xiyu Zhang, Xing Xing · 2024
Selecting a reliable web service has become increasingly harder because of the rapid expansion of web services. It calls for a precise method of decision-making that carefully considers web services from every angle. More research is required to create more realistic service selection outcomes because the current approaches' extreme complexity and limits make the process less trustworthy. The selection of web services using a hybrid multi-criteria decision making strategy is examined in this research. The web services are assessed and chosen using the Technique for Order Preference by Similarity to Ideal Solution approach (TOPSIS), and the weight coefficients are calculated using the Best-Worst Method (BWM) in conjunction with the Gray Wolf Optimization Algorithm (GWO). This paper aims to validate the proposed model by using data from the QWS dataset. We also test the proposed methodology in terms of comparative analysis, with the comparative analyses demonstrating that the method can produce solutions that are more in agreement with existing solutions and improves the effect of service selection.