Performance Comparison of Spider Monkey Optimization and Genetic Algorithm for Traveling Salesman Problem
Wawan Firgiawan, Sugiarto Cokrowibowo, Arnita Irianti, Ahmad Gunawan · 2021
This paper is made to compare the Genetic Algorithm (GA) and Spider Monkey Optimization (SMO) for Traveling Salesman Problem. The background of the selection of these two algorithms based on the resulting performance has been recognized in the literature. In this study, 33 experiment were conducted where each experiment on the dataset was carried out for 10 iterations and then the Average Tour Cost value was taken from all experiments carried out. Based on the Average Tour Cost test, GA has a better performance than SMO with a value of 30.91 and 30.94 (example in the Burma dataset14). However, in terms of testing a larger dataset, the results of ATC SMO values are better than GA with values of 46303,47 and 53592,29 (example in dataset d198). This paper also examines the comparison of the results of the Minimum Tour Cost between GA and SMO. Where SMO got 73% (Win 23 Dataset), GA 27% (Win 9 Dataset), and 3% had the same Minimum Tour Cost. From all these experiments, it can be concluded SMO is the best solution in solving TSP.