Enhancing Kepler Optimization: An Improved Algorithm with Superior Search Capabilities

Abstract

To enhance the global search capability, convergence accuracy, and stability of optimization algorithms, this paper improves the basic Kepler optimization algorithm and proposes an enhanced Kepler optimization algorithm. This algorithm incorporates a tent map to enhance the uniformity and diversity of population initialization; utilizes Lévy flight strategy to balance the algorithm's global exploration and local exploitation capabilities; and designs an adaptive perturbation mechanism to enable the algorithm to effectively escape from local optima in the later stages of iteration. To verify the performance of the proposed algorithm, comprehensive experiments were conducted on the CEC2022 benchmark test function set, and it was compared with six classic swarm intelligence optimization algorithms. The experimental results show that the improved algorithm exhibits significant advantages in convergence speed, solution accuracy, and robustness, especially when dealing with complex, high-dimensional optimization problems. This study provides new insights for the design of optimization algorithms, and the improved algorithm is expected to be widely applied in fields such as engineering optimization and machine learning parameter tuning.