A Method to Minimize Distributed PSO Algorithm Execution Time in Grid Computer Environment
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
The application of swarm intelligence in service-oriented product lines
Proceedings of the 15th International Software Product Line Conference, Volume 2
Accurate sub-swarms particle swarm optimization algorithm for service composition
Journal of Systems and Software
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Grid computing has emerged as a global platform to support organizations for coordinated sharing of distributed data, applications, and processes. Furthermore, Grid computing has also leveraged web services to define standard interfaces for grid services adopting the service-oriented view. Consequently, there have been significant efforts to enable applications capable of tackling computationally intensive problems as services on the Grid. In order to ensure that the available services are optimally assigned to the high volume of incoming requests, it is important to have an efficient service selection algorithm. The algorithm should not only increase access to the distributed services, promoting operational flexibility and collaboration, but should also allow service providers to scale efficiently to meet a variety of demands while adhering to certain current quality of service standards. This paper, proposes and compares two service selection algorithms on the Grid: the Multiple Objective Particle Swarm Optimization algorithm using Crowding Distance technique (MOPSO-CD) to the Constraint Satisfaction based Matchmaking (CS-MM) algorithm.