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Travelling Salesman Problem (TSP) is one of the main and famous problems in finding the shortest path. But life is not ideal, so in this paper, we proposed design a Semantic Travelling Salesman Problems (STSP). The STSP enhances nodes by suitable Ontologies according to a real problem to be more flexible. Also, we proposed a hybrid model (ACSGA) combining the ant colony system (ACS) with the genetic algorithm (GA). That is to speed-up convergence and finds an effective path of a Semantic Travelling Salesman Problem STSP. The performance of the proposed hybrid model ACSGA was faster by 66% than other standard algorithms. And the distance decreases by 0.72%. We apply the proposed solution to the real problem with challenges as traffic overcrowding and weather changeable. So, the proposed STSP in this paper used traffics and weather Ontology as a case study. The proposed method modified the results according to time which decreases by 2.3%.

Smart Solution for STSP Semantic Traveling Salesman Problem via Hybrid Ant Colony System with Genetic Algorithm