Comparing Performance of Evolutionary Algorithms - A Travelling Salesman Perspective

The recent advancements in the fields of computer science and engineering have led to strides in several multi-disciplinary and trans-disciplinary scientific research areas. The Travelling Salesman Problem is one such classical optimization problem that is widely used in various fields including Social Network analysis, traffic flow control, human resource management, logistic planning, etc. Attempts have been made in the past to solve this problem with different computational techniques including basic dynamic programming, neural networks to several others. This paper presents a comparison of four optimization algorithms in solving the Travelling Salesman Problem. The chosen approaches are the classical pure Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant-Colony Optimization (ACO) and the Simulated Annealing algorithm (SA). The paper compares and contrasts the performance based on the problem-solving exercises.