Efficient Service Composition Optimization under Uncertain Environment

Abstract

Over the past few years, companies have been offring a variety of functionalities through Web services as part to stay competitive. Meanwhile, the customers may face challenges in choosing a service that meets their requirements due to the similarity of functionalities. Moreover, the customers’ requests often emerge with a complex nature in an uncertain environment that is rarely fulfilled by an atomic service. Hence, there is a necessity for optimizing service composition based on Quality of Service (QoS) with taking into account environmental uncertainty. To attain the mentioned goal, our proposed approach adopts a local and global optimization strategies. Firstly, we adopt a heuristic based on the Entropy, Cross-Entropy, and the deviation degree for the hesitant fuzzy set to rank similar Web services. Afterwards, we introduce an improved metaheuristic called Group Leaning based Composition as a global optimization for selecting the near-optimal composition.

Key words : Deviation degree, Global Optimization, Group learning metaheuristic, Hesitant fuzzy set, Local Optimization, Service Composition, Quality of Service, Web Service.

Received Date: April 09, 2024                  Accepted Date: May 07, 2024            

Published Date: June 01, 2024

Available Online at: https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/150

DOI:https://doi.org/10.5281/zenodo.11217801