Affiliations: National Engineering Research Center for Equipment and Technology of Cold Strip Rolling, Yanshan University, Qinghuangdao, Hebei, China
Correspondence:
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Corresponding author. Xi Wang, National Engineering Research Center for Equipment and Technology of Cold Strip Rolling,Yanshan University, Qinghuangdao, Hebei 066004, China. Tel.: +86 18677673914; Fax: +86 335 8060089; E-mail: 363109301@qq.com.
Abstract: Differential evolution algorithm (DE) has yielded promising results for solving nonlinear, non-differentiable and multi-modal optimization issues. Due to its simple structure, fast convergence and strong robustness, DE has received increasing attention and wide application in a variety of fields. We propose a novel differential evolution approach (SE-DE) which uses an external archive for opposition-based learning, by this way, more high quality solutions can be selected for candidate solutions. In addition, the mutation factor (F) is adaptively controlled based on the success of offspring/trial solutions generated. An optimization factor α is proposed to select the crossover strategy, a combination of binomial and exponential crossover can effectively balance the exploration and exploitation ability of the algorithm. The performance of SE-DE is compared with the other five DE algorithms including DE, SADE, ODE, NDE and MDE-pBX. The comparison is carried out for a set of 30-, 50- and 100-dimensional test functions from CEC2005. The results show that our algorithm is better than, or at least comparable to, the algorithms from other literature.
Keywords: Differential evolution (DE), self adaptive, external archive, global optimization, unconstrained single objective optimization
