Mokhnachi islam Trad Taj eddine aboulkacem2025-02-092025-02-092024http://dspace.univ-khenchela.dz:4000/handle/123456789/7805Web service selection is a crucial problem in the context of service-oriented architectures, where efficiently identifying the best web service from thousands of available options is essential. This problem is significant in various domains such as e-commerce, e-learning, and e-health, where services must meet diverse quality of service (QoS) criteria including performance, reliability, cost, and availability. The challenge lies in the heterogeneity of services and the need to optimize multiple conflicting objectives. Recently, meta-heuristic algorithms inspired by natural processes have shown great promise in solving complex optimization problems. This work proposes a novel approach using the Tuna Swarm Optimization (TSO) algorithm to address the web service selection problem. The TSO algorithm aims to optimize the selection process by balancing multiple QoS criteria and improving the overall service quality. To achieve this, the TSO algorithm employs a fitness function that incorporates various QoS parameters and guides the search process to identify the optimal web service. The performance of the TSO algorithm is evaluated against other selection algorithms, such as Genetic Algorithm, Particle Swarm Optimization, and Ant Colony Optimization. The results demonstrate that the TSO algorithm is a promising solution for web service selection, offering superior performance in terms of optimizing multiple QoS criteria and efficiently finding the best service.enThesis