Solving the Shortest Path Problem by Fuzzy Ant Colony Optimization Algorithm | ||
| Alfarama Journal of Basic & Applied Sciences | ||
| Article 8, Volume 3, Issue 1, January 2022, Pages 88-104 PDF (920.54 K) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ajbas.2021.91699.1063 | ||
| Authors | ||
| eman yousif darwish* 1; Mohammed Elsayed Wahed2; Ahmed Salama3 | ||
| 1Mathematics & Computer Sciences , Faculty of science , Port Said University | ||
| 2Faculty Of Computers and Informatics, Suez Canal University, Ismailia , | ||
| 3Department of Mathematics, Faculty of Sience, Port Said University; Port Said, Egypt | ||
| Abstract | ||
| I will offer a new strategy for addressing the shortest path problem in two methods in this paper. For the shortest path issue in an uncertain environment, the first technique is the direct graph, and the second method is the indirect graph, and this method is compromised by the Fuzzy Dijkstra algorithm [10]. I'll start by giving a quick overview of fuzzy ant colony methods in general. Eventually, I'll discuss the benefits and drawbacks of employing fuzzy and ant colony algorithms to solve the shortest path problem, as well as my thoughts on the solutions' utility and the future of this field of computer science. The Dijkstra algorithm is a popular method for solving the shortest route problem (SPP). In this research, we use fuzzy ant colony techniques SPP in an uncertain environment and use the Fuzzy Dijkstra algorithm to compromise the output. One is figuring out how to add two edges together. The other problem is determining how to compare the distance between two pathways whose edge lengths are represented by fuzzy integers and fuzzy ant colony techniques. Two numerical examples of a transportation network are utilized to tackle these challenges and demonstrate the efficacy of the proposed strategy. | ||
| Keywords | ||
| fuzzy programming; ant colony algorithms; shortest path; optimization problems | ||
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