文章基本信息

标题：Convergent Stochastic Differential Evolution Algorithms
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作者：Liang Sun ; Hongwei Ge ; Limin Wang 等
期刊名称：International Journal of Hybrid Information Technology
印刷版ISSN：1738-9968
出版年度：2016
卷号：9
期号：7
页码：191-206
DOI：10.14257/ijhit.2016.9.7.18
出版社：SERSC
摘要：Differential evolution (DE) algorithms have been extensively and frequently applied to solve optimizationproblems. Theoretical analyses of their properties are important to understand the underlying mechanismsand to develop more efficient algorithms. In this paper, firstly, we introduce an absorbing Markovsequence to model a DE algorithm. Secondly, we propose and prove two theorems that provide sufficientconditions for DE algorithm to guarantee converging to the global optimality region. Finally, we design two DE algorithms that satisfy the preconditions of the two theorems, respectively. The two proposed algorithmsare tested on the CEC2013 benchmark functions, and compared with other existing algorithms.Numerical simulations illustrate the converge, effectiveness and usefulness of the proposed algorithms.
关键词：Differential evolution; Absorbing Markov sequence; Global search; Local ; search; Convergent