Improving the Arithmetic Optimization Algorithm by combining the Genetic algorithm in the selection of quality-based web services

Abstract

In service-oriented software architectures, the overall quality of the software is intrinsically tied to the quality of the services employed. Given that basic services frequently do not adequately address the varied requirements of users, it is crucial to identify the most effective combination of these services to optimize task performance and enhance service quality within software applications. The task of selecting the most suitable web service is classified as an NP-Hard problem, and an effective strategy to tackle this challenge involves the application of meta-heuristic or evolutionary algorithms. These evolutionary techniques, known for their strong search capabilities, have proven effective in improving selection precision in this area. This study integrates the Arithmetic Optimization Algorithm, which utilizes mathematical operators, with the Genetic Algorithm to bolster extraction efficiency. The analysis of outcomes across different web services reveals that the proposed approach achieves a higher degree of convergence with improved accuracy, yielding an enhancement of more than 1% in precision.