QoS-Aware Middleware for Web Services Composition
IEEE Transactions on Software Engineering
Efficient algorithms for Web services selection with end-to-end QoS constraints
ACM Transactions on the Web (TWEB)
Adaptive Service Composition in Flexible Processes
IEEE Transactions on Software Engineering
A review of particle swarm optimization. Part I: background and development
Natural Computing: an international journal
An Adaptive Service Selection Approach to Service Composition
ICWS '08 Proceedings of the 2008 IEEE International Conference on Web Services
An optimal QoS-based Web service selection scheme
Information Sciences: an International Journal
On optimal decision for QoS-aware composite service selection
Expert Systems with Applications: An International Journal
QSSA: A QoS-aware Service Selection Approach
International Journal of Web and Grid Services
A relaxable service selection algorithm for QoS-based web service composition
Information and Software Technology
A QoS-aware composition method supporting cross-platform service invocation in cloud environment
Journal of Computer and System Sciences
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Web services are emerging technologies that enable application to application communication and reuse of autonomous services. Web services composition is a concept of integrating component services to conduct complex business transactions based on functionality and performance constraints. With the rapid growth of Web services on the Internet, the services owing the same functionality and different performance become more and more, and different users care for different quality. Therefore, designing effective decision-making method for the personalized composite service has become a fundamental problem for the application based on Web service. In this paper, oriented to the user need with single optimization objective, an efficient algorithm, which consists of Particle Swarm Optimization (PSO) and Niche technology, is presented to solve the Web service selection problem. Furthermore, in view of the practical composition requirements including multiple optimization objectives, an algorithm used to resolve the service selection with multi-objective multi-constraint is designed based on NPSO and the intelligent optimization theory of multi-objective PSO, which can produce a set of Pareto optimal composite services by means of optimizing various objective functions simultaneously. Experimental results show that NPSO algorithm owns better global convergence ability with faster convergence speed. Meanwhile, multi-objective multi-constraint NPSO is both feasible and efficient.