The Traveling Salesman Problem(TSP) is one of the most famous combinatorial optimization problems. Optimization can be seen as a generalization of the decision problem; finding solutions with the best objective function values is the objective of optimization problems. Ant colony optimization(ACO) is one of the algorithms which is used to solve discrete optimization problems. The foraging habits of ants serve as an inspiration for ACO. Further, using specialized global optimization to fine-tune the ACO's parameters will help to improve the solution to the traveling salesman problem. The Symbiotic Organism Search(SOS) algorithm is an optimization technique that can be used to tune the parameters of the ACO. This work uses naturally inspired algorithms such as Ant colony optimization and different global and local optimization techniques to solve the Travelling Salesman Problem (TSP). The work also experiments with a hybrid SOS-ACO algorithm and compares it with various ACO algorithm variants. In an effort to enhance ACO's results in tackling the TSP, local optimization techniques are also integrated with the implemented algorithms. On each algorithm that is used in this research, analysis is conducted based on both performance and computation duration. Analysis of the results reveals that the basic ACO algorithm improves after integrating SOS with it to create hybrid SOS-ACO algorithm which further improves when local optimization techniques are used. The nearest neighbour optimization technique works the best with the hybrid SOS-ACO algorithm achieving an error of only 0.67 with the optimal answer in case of one of the testcases.# Travelling-Salesman-Problem-Using-SOS-ACO
Collaborators
- Alimurtaza Mustafa Merchant
- Aprameya Dash
- Suyash Chintawar