Using Optimization Algorithms for Solving Shortest Path Problems | ||||
| Alfarama Journal of Basic & Applied Sciences | ||||
| Article 12, Volume 3, Issue 1, January 2022, Page 138-151 PDF (591.75 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ajbas.2021.90703.1062 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
eman yousif darwish  1; Mohammed Elsayed Wahed2; Ahmed Salama3
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| 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 | ||||
| in this paper I will present two different genetic and ant colony algorithms for solving a classic computer science problem: shortest path problems. I will first give a brief discussion on the general topics of the shortest path problem, genetic and ant colony algorithms. I will conclude by making some observations on the advantages and disadvantages of using genetic and ant colony algorithms to solve the shortest path problem and my opinion on the usefulness of the solutions and the future of this area of computer science . we present two different techniques for solving this problem. The first methods can be solved by using Fuzzy Optimization (FO) as routing protocol with network coding has been investigated and the performance for a proposed network in term of packet delay and throughput and bandwidth consumption has been presented. The second method is based on GA by using crossover , mutation and Selection operator will help in determining which solutions are to be preserved and allowed to reproduce and which ones deserve to die out. Also, it will help in focusing research in promising areas of the search space. | ||||
| Keywords | ||||
| genetic programming; ant colony algorithms; shortest path; optimization problems | ||||
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