This project consists of an implementation of the Ant Colony Optimization (ACO) and genetic algorithm on the Oliver30 dataset of a symmetric travelling salesman problem. These two optimization algorithms are then compared on the metrics of their performance, such as time taken to achieve the shortest distance and the actual shortest distance achieved for the travelling salesman problem. The Traveling Salesman Problem is a NP-hard problem which implies that the problem’s solution is exponential. The use of probabilistic algorithms such as the ones implemented in this project, i.e., genetic algorithms and ant colony optimization (ACO), prove to be extremely efficient in improving the solution. Refer to the attached pdf documents for more information on the study conducted.
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