An Enhanced Ant Colony Optimization Based Algorithm to Solve QoS-Aware Web Service Composition
Fadl Dahana b, Khalil M. El Hindic, Ahmed Ghoneimc, Hussain AlSalmanc
- aCollege of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
- bFaculty of Computing and Information Technology Alturbah, Taiz University, Taiz, Yemen
- cCollege of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
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Abstract
Web Service Composition (WSC) can be defined as the problem of consolidating the services regarding the complex user requirements. These requirements can be represented as a workflow. This workflow consists of a set of abstract task sequence where each sub-task represents a definition of some user requirements. In this work, we propose a more efficient neighboring selection process and multi-pheromone distribution method named Enhanced Flying Ant Colony Optimization (EFACO) to solve this problem. The WSC problem has a challenging issue, where the optimization algorithms search the best combination of web services to achieve the functionality of the workflow's tasks. We aim to improve the computation complexity of the Flying Ant Colony Optimization (FACO) algorithm by introducing three different enhancements. We analyze the performance of EFACO against six of existing algorithms and present a summary of our conclusions.
TL;DRThis work proposes a more efficient neighboring selection process and multi-pheromone distribution method named Enhanced Flying Ant Colony Optimization (EFACO) to solve the problem of consolidating the services regarding the complex user requirements. (Semantic Scholar)