关键词: 帝国竞争算法, 春秋战国, 史实, 帝国同化, 跳出局部最优

Abstract: Aiming at the problem that imperialist competitive algorithm falls into the curse of dimensionality easily when used for solving high-dimensional functions because of premature convergence, an improved imperialist competitive algorithm inspired by the historical facts of the vassal states vying for power in the Spring and Autumn Period of China was proposed. Firstly, the competition strategy of "Cooperative Confrontation" was introduced in the process of country initialization to enhance the information interaction, so as to preserve the high-quality populations. Secondly, learnt from the colonial rule strategy of gradual infiltration and assimilation at all levels of state was used in the empire assimilation process to improve the development ability of the algorithm. Finally, the mechanism of judging and jumping out of local optimum was added to avoid the impact of "premature" on the optimization performance. In the simulation experiment, through 8 classic benchmark functions, the optimization ability, convergence speed and high-dimensional function applicability of the improved algorithm were verified, and three schemes for jumping out of local optimum were compared and analyzed. Furthermore, CEC2017 test function experiment was carried out, and the comparison of the proposed improved algorithm with five representative advanced algorithms in the field of algorithm improvement in recent years showed that the improved algorithm had higher optimization accuracy and stronger stability. And the Kendall correlation coefficient verification showed the significant difference between the improved algorithm and the original algorithm on optimization performance, and that the improvement of assimilation mechanism played a key role in the performance improvement.

Key words: Imperialist Competitive Algorithm (ICA), Spring and Autumn Period, historical fact, empire assimilation, jump out of local optimum