A Quasi-Oppositional Learning-based Fox Optimizer for QoS-aware Web Service Composition in Mobile Edge Computing

Abstract

Currently, web service-based edge computing networks are across-the-board, and their users are increasing dramatically. The network users request various services with specific Quality-of-Service (QoS) values. The QoS-aware Web Service Composition (WSC) methods assign available services to users’ tasks and significantly affect their satisfaction. Various methods have been provided to solve the QoS-aware WSC problem; However, this field is still one of the popular research fields since the dimensions of these networks, the number of their users, and the variety of provided services are growing outstandingly. Consequently, this study presents an enhanced Fox Optimizer (FOX)-based framework named EQOLFOX to solve QoS-aware web service composition problems in edge computing environments. In this regard, the Quasi-Oppositional Learning is utilized in the EQOLFOX to diminish the zero-orientation nature of the FOX algorithm. Likewise, a reinitialization strategy is included to enhance EQOLFOX's exploration capability. Besides, a new phase with two new movement strategies is introduced to improve searching abilities. Also, a multi-best strategy is recruited to depart local optimums and lead the population more optimally. Eventually, the greedy selection approach is employed to augment the convergence rate and exploitation capability. The EQOLFOX is applied to ten real-life and artificial web-service-based edge computing environments, each with four different task counts to evaluate its proficiency. The obtained results are compared with the DO, FOX, JS, MVO, RSA, SCA, SMA, and TSA algorithms numerically and visually. The experimental results indicated the contributions' effectiveness and the EQOLFOX's competency.