关键词: 帝国竞争算法, 早熟收敛, 微分进化, 克隆进化

Abstract: To deal with the problem of premature convergence and low precision of the traditional imperialist competitive algorithm (ICA), this paper proposes two improved ICAs based on biological evolution. In the traditional ICA, colony revolution will lead to low precision because the operator may make the strong colony lost. To overcome this shortcoming, a differential evolution operator is introduced, which makes use of the interaction among colonies to produce new colonies. The operator will enhance the population diversity and keep the excellent individuals at the same time. Furthermore, on account of strengthening the interaction among empires, a clone evolution operator is introduced, which includes the following steps: clonal reproduction of the stronger countries; high frequency variation and random crossover of clonal populations; the stronger countries take place of the weaker ones. The operator can guide the search for global optimum efficiently. The proposed methods are applied to six benchmark functions and six typical complex function optimization problems, and the performance comparison of the proposed methods with other ICAs is experimented. The results indicate that the proposed methods can significantly speed up the convergence and improve the precision and stability.

Key words: imperialist competitive algorithm, premature convergence, differential evolution, clone evolution