Biogeography-based learning particle swarm optimization method for solving dynamic optimization problems in chemical processes

CHEN Xu; MEI Congli; XU Bin; DING Yuhan; LIU Guohai

doi:10.11949/j.issn.0438-1157.20161786

您当前的位置：

首页 >

文章列表页 >

Biogeography-based learning particle swarm optimization method for solving dynamic optimization problems in chemical processes

更新时间：2025-12-10

- Biogeography-based learning particle swarm optimization method for solving dynamic optimization problems in chemical processes
- CIESC Journal Issue 8, Pages: 3161-3167(2017)
- 作者机构：
  
  1. 江苏大学电气信息工程学院,江苏,镇江,212013
  2. 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
  3. 上海工程技术大学机械工程学院,上海,201620
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20161786
  CLC： Q021.8
- Online First：05 August 2017，
  
  Published：05 August 2017
- 稿件说明：
移动端阅览
CHEN Xu, MEI Congli, XU Bin, et al. Biogeography-based learning particle swarm optimization method for solving dynamic optimization problems in chemical processes[J]. CIESC Journal, 2017, (8): 3161-3167.
DOI：

CHEN Xu, MEI Congli, XU Bin, et al. Biogeography-based learning particle swarm optimization method for solving dynamic optimization problems in chemical processes[J]. CIESC Journal, 2017, (8): 3161-3167. DOI： 10.11949/j.issn.0438-1157.20161786.

摘要

智能优化算法具有适用性广泛、全局搜索能力强等优点，近年来在动态优化中的应用逐渐增多。通过混合生物地理优化与粒子群优化，提出了生物地理学习粒子群（biogeography-based learning particle swarm optimization，BLPSO）算法，并用于动态优化问题的求解。BLPSO采用了新型的生物地理学习方式，该方式根据粒子“排名”，即粒子的优劣，以维度为单位构造学习粒子，提高了学习的效率。针对动态优化问题，首先通过控制向量参数化将其转化为非线性规划问题，然后采用BLPSO算法进行求解。最后，将BLPSO应用于非可微、多峰、多变量等典型动态优化问题的求解，计算结果表明BLPSO具有较好的搜索精度和收敛速度。

Abstract

Intelligent optimization algorithms have been playing an increasing role in dynamic optimization

due to advantages of wide applicability and strong global searching capability. Biogeography-based learning particle swarm optimization (BLPSO) was proposed for dynamic optimization problems (DOPs) by hybridizing biogeography-based and particle swarm optimization. BLPSO employed a new biogeography-based learning approach for construction of learning examples by ranking of particles (i.e.

the quality of particles) and dimension as unit

such that learning efficiency was enhanced. Control vector parameterization first converted DOPs into nonlinear programming problems which were then solved by BLPSO. The simulation results on typical DOPs with non-differentiable

multi-modal and multi-variable characteristics show that BLPSO has outstanding solution precision and convergence speed.

关键词

Keywords

references

Views

196

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A variable time nodes control vector parameterization approach for solving optimal control problems

Multi-objective optimization of extractive distillation for norbornene-cyclohexane separation

Prediction of biomass pyrolysis product yield based on machine learning algorithms

Deep reinforcement learning-based intelligent production scheduling for a heterogeneous production line plants

Chemical process data imputation method based on DAT-MIGAN model

Related Author

LI Guodong

LIU Xinggao

LI Jinming

WANG Jian

QU Yajuan

DU Jinze

CONG Haifeng

LI Xingang

Related Institution

State Key Laboratory of Industry Control Technology, Department of Control, Zhejiang University

School of Chemical Engineering, Tianjin University

Wanhua Chemical Group Co., LTD

Zhejiang Institute of Tianjin University

Institute of Agricultural Environment and Sustainable Development, Chinese Academy of Agricultural Sciences

⁰