Comparative Analysis of Meta-Heuristic Algorithms for Solving Optimization Problems
- 10.2991/meici-18.2018.121How to use a DOI?
- Ant Colony Algorithm (ACO), Genetic Algorithm (GA), Simulated Annealing Algorithm (SAA), Traveling salesman Problem (TSP), Meta-Heuristics, Optimization
Combinatorial Problems (NP hard Problem) have always been a hard task to be solved to optimal level but for the efficiency and finding the best possible solution in a certain span of time it has been solved to suboptimal level. During the study for solving the combinatorial problems to suboptimal level different heuristic algorithms has been used for acquiring results from the TSPLIB Instances. Different Suboptimal level has been achieved through different heuristics like Ant Colony Algorithm, Genetic Algorithm and Simulated Annealing Algorithm. The perimeters were tuned to different levels of all heuristics to find suboptimal level of the instances of TSPLIB. The paper will also present the effects of perimeters tuning to achieve the suboptimal results.
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Kashif Muhammad AU - Shang Gao AU - Sohail Qaisar AU - MannanMasood Abdul AU - Atif Muhammad AU - Ashraf Usman AU - Akhtar Aleena AU - Ali Shahid PY - 2018/12 DA - 2018/12 TI - Comparative Analysis of Meta-Heuristic Algorithms for Solving Optimization Problems BT - Proceedings of the 2018 8th International Conference on Management, Education and Information (MEICI 2018) PB - Atlantis Press SP - 612 EP - 618 SN - 1951-6851 UR - https://doi.org/10.2991/meici-18.2018.121 DO - 10.2991/meici-18.2018.121 ID - Muhammad2018/12 ER -