<![CDATA[The Travelling Salesman Problem (TSP) is one of the well-known problems in operational research. This problem arises in various applications such as telecommunications, electronics, logistics, transportation, astronomy, industry, and so on. The TSP involves a salesman who must visit all cities, with each city being visited exactly once, and the salesman must start and return to the origin city. The goal is to determine the route with the minimum travel distance or total cost. Cat Swarm Optimization (CSO) is an optimization algorithm based on the natural behavior of cats. This behavior is modeled in two submodels: the seeking mode and the tracing mode. Discrete Cat Swarm Optimization (DCSO) is a version of CSO that uses integer encoding. Simulated Annealing (SA) is a method used to find the global minimum of a cost function that may have local minima. SA works by mimicking a physical process in which a solid is slowly cooled so that, eventually, the structure freezes into a configuration with the minimum energy. Since the solutions produced by the DCSO algorithm may get trapped in a local optimum, and SA is capable of finding the global minimum of a solution from a local minimum, this research aims to solve the TSP by combining (hybridizing) both the DCSO and SA algorithms. The hybrid process is carried out by performing the SA process after the DCSO iterations, except for the final iteration. The hybrid process of DCSO and SA begins with the initialization of parameters, input data, followed by the seeking mode and tracing mode. The worst result from DCSO will then be processed using the SA algorithm.]]>
