中文摘要：

针对二阶分布估计算法的早熟收敛问题,提出一种基于混合采样机制的互信息分布估计算法（MIEDA）.MIEDA利用互信息度量变量之间的相关性,形成互信息树的概率模型;采用稀疏模型构建的思想,并基于自私基因理论建立信息奖惩机制,以加快算法的收敛速度;结合反向学习、最优解变异和随机采样形成混合采样机制,以提高算法的采样效率.仿真结果表明,MIEDA比常见的二阶分布估计算法具有更高的稳定性和更强的寻优能力.

英文摘要：

A mutual information estimation of distribution algorithm（MIEDA） with hybrid sampling mechanism is proposed to overcome premature convergence of second order estimation of distribution algorithms. The M1EDA firstly uses mutual information to measure the interaction between two variables, which can generate mutual information tree model. Then, based on the concept of sporadic model building and a reward and punishment scheme in the selfish gene, the MIEDA can accelerate the convergence speed. Finally, a hybrid sampling mechanism is also adopted in the MIEDA to improve the efficiency of sampling, which combines stochastic sampling, the opposition-based learning（OBL） scheme and mutation on the current optimal individual. The simulation results show that, compared with several other second order algorithms, the MIEDA often performs better in convergent reliability and search ability.