Multi-strategy Improved Kepler Optimization Algorithm

To solve the problem of falling into local optima and slow convergence speed in the standard Kepler optimization algorithm, the improved Kepler optimization algorithm is proposed. Firstly, the random number generator is replaced by the circle mapping and reverse learning strategy in the initial population stage. Secondly, the introduction of adaptive mutation rate and arctangent decay factor in the stage that planets update positions, which adjust the search space of the algorithm in the early and late stages, improves the convergence speed of the algorithm. Thirdly, Gauss mutation is performed on the position of the optimal solution to improve the algorithm’s tendency to fall into local optima. Finally, improved Kepler optimization algorithm, and four other optimization algorithms, are comprehensively tested on 8 benchmark functions. The experimental results show that the proposed algorithm has higher convergence speed and accuracy.