| Autor: |
Drira, Rim, Gabsi, Hamdi, Ghezala, Henda Hajjami Ben |
| Zdroj: |
International Journal of Web Science; 2022, Vol. 3 Issue: 3 p204-235, 32p |
| Abstrakt: |
Cloud manufacturing (CMfg) aims to create dedicated manufacturing solution with lower operating cost and faster time-to-market by combining available manufacturing resources and capabilities. In order to insure a dedicated manufacturing cloud matching the business' requirements, cloud service composition is considered a decisive process. Due to the large diversity of resources within similar functions and different quality of services (QoSs), the composition process can be a challenging task. In fact, this is known as composition plan selection (CPS) problem. The need for timely resolving this problem motivates the adoption of meta-heuristics algorithms like artificial bee colony (ABC) and genetic algorithm (GA). We propose in this paper, an improved ABC algorithm for the CPS problem which considers both business and QoS attributes for services composition. Experiments are done in order to tune precisely our algorithm and to compare it with similar state of the art algorithms. |
| Databáze: |
Supplemental Index |
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