Performance Comparison of Ant Colony Optimization Algorithm Through Different Local Search Mechanisms for Traveling Salesman Problem

The traveling salesman problem (TSP) in combinatorial optimization is an NP-hard problem has been used to numerous science and technology fields. Actually, it is fairly simple to comprehend yet frequently very difficult to resolve. Numerous algorithms have been created and evolved due to their complexity and usefulness. One of the most algorithms for solving traveling salesman problem (TSP) is Ant Colony Optimization Algorithm (ACO). In this project thesis, we study on local search operators for solving TSP problem. In fact, we use three operators which are Swap, Reversion, and Insertion to find out the best possible solutions of the TSP problem. The algorithms are tested on benchmark problems taken from TSPLIB. It is observed from the experimental results that, the Swap operator is better than other operators in terms of both solution quality as well as computational time.

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