Solving the Robust Design problem for MMSFNs by Using RWGA | ||||
| Sohag Journal of Sciences | ||||
| Volume 10, Issue 1, March 2025, Page 166-171 PDF (477.22 K) | ||||
| Document Type: Regular Articles | ||||
| DOI: 10.21608/sjsci.2024.326281.1225 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Hameda A.M. El- Sennary; Noha Orabi; Moatamad R. Hassan  ; H. Abdou
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| Department of Computer Science and Mathematics, Faculty of Science, Aswan University, Aswan, Egypt | ||||
| Abstract | ||||
| Creating a reliable flow network is believed to be an NP-hard problem. The optimal capacity needs to be allocated in order to optimize network reliability. MMSFNs (multi-source multi-sink flow networks) are used in a wide range of real-world systems, such as computer transportation, telecommunication systems, and logistics. The optimal capacity problem for MMSFNs has been discussed and solved by using a single genetic-based approach. The random weight genetic algorithm (RWGA), one of the methods used in the multi-objective genetic algorithm (MOGA) to solve the resilient design in MMSFNs, served as the foundation for the algorithm we created in this work. In order to maximize MMSFN system reliability, the suggested algorithm aims to reduce the overall capacity (nodes) of the components. The proposed RWGA-based approach applied to three different networks with different topologies: two-source two-sink network, three-source two-sink network, and two-source three-sink network and information to verify its effectiveness. In contrast to previous efforts solving the problem, the obtained outcomes are better. | ||||
| Keywords | ||||
| Robust Design; Flow Network; MMSFNs; Capacity assignment; RWGA | ||||
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