SailFish Optimizer Algorithm to Solve the Traveling Salesman Problem

The traveling Salesman Problem is NP-hard and a well-known problem in combinatorial research. In order to find the right solution, the best-known methods are to make a list of all possible solutions, but the disadvantage of this method is that the resolution time becomes excessively long. To deal with these obstacles, researchers are working on a new methods class named meta-heuristic, to try solving a wider range of these unresolved problems. A recent one called Sailfish optimizer algorithm (SFO), which is inspired by nature, in particular based on the behavior of sailfish hunting. In this work, we present a first adaption of this algorithm for discrete case and especially to resolve travelling salesman problem, the obtained results show the efficiency of the proposed adaptation to solve some instances of this problem and the results was compared with other metaheuristics methods.