摘要
针对多目标分布估计算法全局收敛性较弱的缺陷,提出了一种自适应混合多目标分布估计进化算法。其基本思想是:在多目标分布估计算法中引入全局收敛性较强的差分进化算法,当函数变化率较大时,用分布估计算法产生新种群;当函数变化率较小即算法可能陷入局部收敛时,用差分进化算法产生新种群。理论分析和数值实验结果表明,这种混合算法不仅具有良好的全局收敛性,而且解的分布性和均匀性较没有考虑目标函数变化率的混合多目标分布估计算法也有了一定程度的提高。
An adaptive hybrid multi-objective estimation of distribution evolutionary algorithm based on the change rate of objective function is put forward for overcoming the defect in global convergence of multi-objective estimation of dis-tribution algorithm. The basic idea of new method is that differential evolution algorithm is introduced into multi-objective estimation of distribution algorithm. When the change rate of function is large, new population is generated with estima-tion of distribution algorithm, and otherwise differential evolution algorithm is used to generate new population. Theoreti-cal analysis and numerical results show that the hybrid algorithm has better global convergence, and the distribution and uniformity of solutions is improved to a certain extent compared with algorithm without considering the change rate of objective function.
出处
《计算机工程与应用》
CSCD
2014年第5期46-50,207,共6页
Computer Engineering and Applications
基金
安徽省教育厅自然科学基金项目(No.2012kb236)
关键词
多目标优化
分布估计算法
差分进化算法
自适应
函数变化率
multi-objective optimization
estimation of distribution algorithm
differential evolution algorithm
adaptive
change rate of function
