A Comparative Study of Three Metaheuristics Applied to the Traveling Salesman Problem
Jérôme Leboeuf Pasquier, Ivica Kalichanin Balich, Donald W. Carr, Cuauhtémoc López‐Martín · 2007
This paper presents a comparative study of three metaheuristics: Genetic Algorithm (GA), Ant Colony Optimization (AC) and Simulated Annealing (SA), implemented to solve the classical Traveling Salesman Problem (TSP). The efficiency of each approach is evaluated taking into account the execution time of the algorithm and the quality of the generated solution. Additionally, metrics of the program, including McCabe Complexity, Development effort and Lines of Code, are calculated to complete the comparative study. Finally, an evaluation of the difficulty of implementation and the quality of the results corresponding to each metaheuristic is given. The present research will help programmers understand, evaluate and implement the three Metaheuristics.