文献类型：会议论文

英文题名：A Hybrid Differential Evolution Algorithm with Invasive Weed Optimization and Its Application to Modeling of Carbon Content

作者：Luo, Leitao[1];Zhang, Lingbo[1];Gu, Xingsheng[1]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China

会议论文集：IEEE International Conference on System Science and Engineering (ICSSE)

会议日期：JUL 11-13, 2014

会议地点：Shanghai, PEOPLES R CHINA

语种：英文

外文关键词：Least squares support vector machines; Differential evolution; Invasive weed optimization; Carbon content

摘要：This paper aims to the prediction of carbon content in spent catalyst in a continuous catalytic reforming (CCR) plant based on least squares vector machines (LSSVM). When modeling by LSSVM, the problem of optimizing the hyper-parameters draws many researchers' attention. In this paper, a novel hybrid algorithm named IWODE is proposed to deal with it. The algorithm embeds invasive weed optimization (IWO) as a local refinement procedure into differential evolution with adaptive crossover rate. New competitive exclusion and adaptive step length of spatial dispersal based on individuals' distance are introduced to make IWO more suitable as a local search algorithm. Simulation results and comparisons based on some well-known benchmarks indicate the efficiency of IWODE. And the predicted results of carbon content using the proposed method agree with the actual values well. The method is compared with five other techniques, including LSSVM optimized by DE, IWO, other two modified versions of DE and back propagation neural network (BPNN). The obtained results demonstrate that the proposed IWODE-LSSVM is superior to others in generalization performance and prediction ability.