Bees Algorithm (BA) is a popular meta-heuristic method that has been used in many different optimization areas for years. In this study, a new version of combinatorial BA is proposed and explained in detail to solve Traveling Salesman Problems (TSPs). The nearest neighbor method was used in the population generation section of BA, and the Multi-Insert function was added to the local search section instead of the Swap function. To see the efficiency of the proposed method, 24 different TSPs were used in experimentation and the obtained results were compared with both classical combinatorial BA and other successful meta-heuristic methods. After detailed analyses and experimental studies on different problems, it has been observed that the proposed method performs well for TSPs and competes well with other methods.
The traveling salesman problem (TSP) has been a popular problem studied in the optimization field for a long time. The most successful methods used in solving these difficult problems are metaheuristic algorithms. In this study, an improved version of the bees algorithm is used in the solution of TSPs. In addition to the classical bees algorithm, two different city selection and relocation functions have been developed. With these functions, it is possible to change the location of multiple and variable numbers of cities. These new functions have been added to the continuation of the classical bees algorithm and are used only on the elite site, ensuring that the elite site becomes more elite. Thus, better results could be obtained with less number of iterations and the number of the total evaluation compared to the existing bees algorithm.
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