Web Service Selection based meta-heuristic methods(TSO)
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Date
2024
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Abstract
Web 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.