改进鲸鱼优化算法及其在渣油加氢参数优化的应用

许瑜飞¹ 钱锋¹ 杨明磊¹ 杜文莉¹ 钟伟民¹

(1.华东理工大学化工过程先进控制和优化技术教育部重点实验室, 上海 200237)

【摘要】针对智能优化算法在处理非线性优化问题中存在的容易陷入局部最优和收敛精度差等问题,提出了一种基于结合差分进化和精英反向学习的改进鲸鱼算法(DEOBWOA)。该算法引入对立搜索初始化、精英反向学习,并结合差分进化进行变异修正,显著有效地提高WOA算法的收敛精度和收敛速度,提高其跳出局部最优的能力。之后采用8个标准测试函数进行仿真实验,结果表明:DEOBWOA算法与标准WOA、HCLPSO、DE算法相比,全局搜索能力和收敛速度都有较大提升。最后建立了渣油加氢动力学模型,考虑到渣油加氢过程中存在诸多典型的非线性约束问题,以某炼化厂渣油加氢装置为例,应用DEOBWOA对渣油加氢反应动力学模型参数进行优化,结果表明该算法能较好地处理实际工程优化问题。

【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;

【DOI】

【基金资助】 国家科技支撑计划项目(2015BAF22B02) supported by the Project of National Research Program of China(2015BAF22B02) 国家自然科学基金项目(61422303,61590922) the National Natural Science Foundation of China(61422303;61590922) 中央高校基本科研业务费专项资金 the Fundamental Research Funds for the Central Universities

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