Abstract
"This paper explores the application of genetic algorithms (GAs) to solve the Traveling Salesman Problem (TSP) and its variants, specifically the Clustered Generalized Traveling Salesman Problem (CGTSP). In CGTSP, nodes (or cities) are grouped into clusters, and the primary objective is to determine the most efficient route that includes precisely one node from each cluster while minimizing the overall travel distance This particular variation plays a crucial role in practical scenarios, such as logistics, vehicle routing, and network design. In these applications, destinations are naturally grouped, and visiting one representative from each group is often essential or mandatory. CGTSP effectively tackles the complexity and real-world constraints more adeptly than the traditional TSP by integrating the element of clustering. This feature makes it a versatile and applicable model for a wide range of industrial and scientific problems. The study aims to assess the effectiveness of Genetic Algorithms (GAs) in solving these complex optimization problems. The paper provides an overview of the TSP and CGTSP, shows how to rewrite a CGTSP in the form of a TSP and vice versa, discusses the fundamentals of GAs, and presents the application of GAs to the problem variants. In addition, we investigate a new method to generate the initial population in the genetic algorithm and evaluate the proposed algorithm’s performance through experimental results. The findings highlight the potential of GAs as powerful tools for solving challenging optimization problems."
Published Version
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