计算机技术与发展Issue(8)：62-65,4.DOI:10.3969/j.issn.1673-629X.2013.08.016
差分进化算法马尔可夫链模型及收敛性分析
Analysis of Differential Evolution's Markov Chain Model and Convergence
摘要
Abstract
As a modern optimization algorithm,differential evolution algorithm which is based on the individual differential reconstruction idea is designed for the global continuous optimization problem. Up to now,the improvement and application of the algorithm are mainly focused by researchers but theoretical analysis of the algorithm is seldom taken into account. In order to analyze the convergence of the al-gorithm,the concepts of state transition for individual and population are defined and the optimal state set of population is proposed. The individual state transition probability is computed according to the operators of differential evolution algorithm. The state sequence of pop-ulation has been proved to be Finite Nonhomogeneous Markov chain and the Markov chain model of differential evolution is proposed. At last,the theory analysis of the differential evolution demonstrates that it is not able to guarantee the global convergence. The result of the theory research shows that keeping the population diversity will improve the performance of the algorithm.关键词
差分进化/马尔可夫链/收敛性分析/全局收敛/局部收敛Key words
differential evolution/Markov chain/convergence analysis/global convergence/local convergence分类
信息技术与安全科学引用本文复制引用
孙成富,赵建洋,陈剑洪..差分进化算法马尔可夫链模型及收敛性分析[J].计算机技术与发展,2013,(8):62-65,4.基金项目
江苏省科技支撑计划(BE2012112) （BE2012112）
淮安市科技支撑计划(工业)项目(HAG2011044,HAG2011045) （工业）
