Improved Kepler Optimization Algorithm Based on Mixed Strategy

The original Kepler optimization algorithm (KOA) is characterized by slow convergence speed, weak global search capability, low solution accuracy, and susceptibility to local optima. In this paper, we propose MSKOA, a hybrid strategy designed to improve the features of the original KOA. Specifically, we adopt a Sobol sequence to initialize the population, aiming to achieve a more uniform distribution of initial solutions across the solution space, and integrate a sine-cosine algorithm with mutation opposition-based learning to enhance both the global search and local exploitation capabilities. The results of experimental comparative analysis on ten benchmark test functions demonstrate that the improved Kepler optimization algorithm based on a mixed strategy exhibits notable improvements in both convergence speed and solution accuracy.