Improved snake optimizer based on forced switching mechanism and variable spiral search for practical applications problems
摘要
In this study, an improved snake optimizer (ISO) enhances snake optimizer (SO) by introducing novel mechanisms for improving convergence ability and stability. Based on the “No Free Lunch” theory, this paper discusses the development status of swarm intelligence algorithms. ISO introducing a chaotic mapping strategy allows the population to generate uniform and randomly distributed initial values at the beginning of ISO. A forced switching mechanism improving the balance between two different updating phases is introduced to address the slow convergence rate based on SO. A variant strategy of whale optimization algorithm is used to make further improvement for exploration of SO. The optimal domain perturbation strategy is used to find a better value near the result after above process. These enhancements result a more robust and effective optimization framework suiting to solve complex continuous optimization problems across various domains. The convergence ability and stability are validated by comparing ISO with SO and eight other classical or latest algorithms with dimensionality setting as Dim = 10 and Dim = 20 in the test functions from CEC-2021. The convergence efficiency achieves 94/160 and the convergence stability achieves 77/160. The ratio of convergence efficiency achieves