Improved whale optimization algorithm and its application in optimization of residue hydrogenation parameters

XU Yufei; QIAN Feng; YANG Minglei; DU Wenli; ZHONG Weimin

doi:10.11949/j.issn.0438-1157.20171128

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Improved whale optimization algorithm and its application in optimization of residue hydrogenation parameters

更新时间：2025-12-10

- Improved whale optimization algorithm and its application in optimization of residue hydrogenation parameters
- CIESC Journal Issue 3, Pages: 891-899(2018)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20171128
  CLC： E624
- Online First：05 March 2018，
  
  Published：05 March 2018
- 稿件说明：
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XU Yufei, QIAN Feng, YANG Minglei, et al. Improved whale optimization algorithm and its application in optimization of residue hydrogenation parameters[J]. CIESC Journal, 2018, (3): 891-899.
DOI：

XU Yufei, QIAN Feng, YANG Minglei, et al. Improved whale optimization algorithm and its application in optimization of residue hydrogenation parameters[J]. CIESC Journal, 2018, (3): 891-899. DOI： 10.11949/j.issn.0438-1157.20171128.

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。

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.

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