Multi-group discrete symbiotic organisms search applied in traveling salesman problems

Recent years have witnessed a growing development in swarm intelligence. With its distinctive advantages in settling complex issues in practice, more and more researchers have paid attention to this point. Compared with other swarm algorithms, the symbiotic organism search (SOS) algorithm, a promising meta-heuristic evolutionary one, is featured by high convergence and high-quality solutions in the overall optimization process. However, when the SOS algorithm was proposed, it was to solve real-life problems, and there are many discrete problems to be solved by us, so some scholars discretized the SOS algorithm. The discretized SOS algorithm has the shortcoming that it is very easy to fall into the local optimum, which leads to insufficient convergence, so in this paper, we propose a multi-group discrete SOS algorithm with gene transfer and path cross for elimination strategies to improve the performance of the DSOS algorithm in solving TSP. To prove the performance and efficiency of the algorithm for the TSP problem, the symmetry problem in the TSPLIB data set is employed to verify it, and this paper compares it with other algorithms to show the practicability of the method.

Automated summary

In recent years, there has been rapid development in swarm intelligence, particularly with the emergence of the symbiotic organism search (SOS) algorithm. While the SOS algorithm has shown promise in solving real-life problems, its discretization poses challenges. This study introduces a multi-group discrete SOS algorithm with gene transfer and path cross strategies to enhance its performance in solving the TSP problem.