Eldem, HüseyinÜlker, Erkan2021-12-132021-12-1320200218-00141793-6381https://doi.org/10.1142/S0218001420590399https://hdl.handle.net/20.500.13091/531It is known that some of the algorithms in optimization field have originated from inspiration from animal behaviors in nature. Natural phenomena such as searching behavior of ants for food in a collective way, movements of birds and fish groups as swarms provided the inspiration for solutions of optimization problems. Traveling Salesman Problem (TSP), a classical problem of combinatorial optimization, has implementations in planning, scheduling and various scientific and engineering fields. Ant colony optimization (ACO) and Particle swarm optimization (PSO) techniques have been commonly used for TSP solutions. The aim of this paper is to propose a new hierarchical ACO- and PSO-based method for TSP solutions. Enhancing neighboring operators were used to achieve better results by hierarchical method. The performance of the proposed system was tested in experiments for selected TSPLIB benchmarks. It was shown that usage of ACO and PSO methods in hierarchical structure with neighboring operators resulted in better results than standard algorithms of ACO and PSO and hierarchical methods in literature.eninfo:eu-repo/semantics/closedAccessAnt Colony OptimizationSwarm IntelligenceNeighborhood OperatorsTraveling Salesman ProblemMetaheuristicParticle Swarm OptimizationHierarchical ApproachTraveling Salesman ProblemOptimization AlgorithmSearch AlgorithmParticle SwarmArticle10.1142/S02180014205903992-s2.0-85083697424