Abstract:

Solving traveling salesman problems with many connections with classical optimization methods is challenging. In this context, meta-heuristic algorithms that direct their searches to an optimum solution are preferred. In this study, the Bee Algorithm, one of these meta-heuristic algorithms inspired by the foraging methods of honey bees, was examined. The study aims to increase the Bees Algorithm's effectiveness in solving traveling salesman problems. The Variable Multiple Insertion operator has been added to the Classical Bee Algorithm, and optimizations have been made for different traveling salesman problems by searching within close neighborhood regions. As a result of the tests, it was seen that much better results were obtained with this algorithm compared to other Bee Algorithms in the literature. The algorithm has reduced from 1.40-2.80% to 0.11-0.50% in problems of 100 cities and from 8.10-9.67% to 2.00-2.79% in issues of 200 cities.

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