帝国竞争算法的进化优化

为了改善帝国竞争算法（imperialist competitive algorithm，ICA）易早熟收敛、精度低等缺点，提出了两种基于生物进化的改进ICA算法。针对殖民地改革算子可能使势力较强的殖民地丢失，导致寻优精度降低的不足，引入了一种微分进化算子，利用殖民地之间的信息交互产生新的殖民地，在增强群体多样性的同时保留了优秀个体。另外，针对帝国之间缺乏有效的信息交互这一情况，引入了克隆进化算子，对势力较强的国家进行克隆繁殖，并经过克隆群体的高频变异和随机交叉，选择势力较强的国家取代势力较弱的国家，从而有效地引导算法向最优解方向搜索。将算法应用于6个基准函数和6个经典复合函数优化问题，并与其他ICA改进算法进行比较，结果表明，基于生物进化的ICA算法在收敛精度、收敛速度及稳定性上有显著提高。

Evolutionary Optimization of Imperialist Competitive Algorithm

To deal with the problem of premature convergence and low precision of the traditional imperialist competi-tive 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.