化工学报2018，Vol.69Issue(3)：891-899,9.DOI:10.11949/j.issn.0438-1157.20171128
改进鲸鱼优化算法及其在渣油加氢参数优化的应用
Improved whale optimization algorithm and its application in optimization of residue hydrogenation parameters
摘要
Abstract
An improved whale algorithm (DEOBWOA) based on differential evolution and elite opposition-based learning is proposed to solve the problem that the intelligent optimization algorithm is easy to fall into the local optimum and the convergence precision in dealing with the nonlinear optimization problem is poor. The algorithm uses the opposing search initialization, elite opposition-based learning and combines with differential evolution, which can improve the convergence precision and convergence speed of the whale optimization (WOA) algorithm effectively and improve the ability to jump out of local optimum. 8 standard test functions are used to do simulation experiment. The results show that DEOBWOA algorithm has a better performance than WOA, heterogeneous comprehensive learning particle swarm optimization (HCLPSO) and differential evolution (DE). Finally, the kinetic model of residue hydrogenation was established, but there are many typical nonlinear constraints in the process of residue hydrogenation. So DEOBWOA was used to optimize the kinetic model parameters of residue hydrogenation in a refinery residue, which indicates the algorithm can deal with the practical engineering optimization problem.关键词
算法/鲸鱼优化算法/渣油加氢/动力学模型/参数估值/优化Key words
algorithm/whale optimization algorithm/residue hydrogenation/kinetic modeling/parameter estimation/optimization分类
能源科技引用本文复制引用
许瑜飞,钱锋,杨明磊,杜文莉,钟伟民..改进鲸鱼优化算法及其在渣油加氢参数优化的应用[J].化工学报,2018,69(3):891-899,9.基金项目
国家科技支撑计划项目(2015BAF22B02) （2015BAF22B02）
国家自然科学基金项目(61422303,61590922) （61422303,61590922）
中央高校基本科研业务费专项资金.supported by the Project of National Research Program of China(2015BAF22B02),the National Natural Science Foundation of China(61422303,61590922)and the Fundamental Research Funds for the Central Universities. （2015BAF22B02）
