Improved biogeography-based optimization algorithm and its application in gasoline blending scheduling

WANG Yumei; CHEN Hui; QIAN Feng

doi:10.11949/j.issn.0438-1157.20151812

您当前的位置：

首页 >

文章列表页 >

Improved biogeography-based optimization algorithm and its application in gasoline blending scheduling

更新时间：2025-12-10

- Improved biogeography-based optimization algorithm and its application in gasoline blending scheduling
- CIESC Journal Issue 3, Pages: 773-778(2016)
- 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.0438-1157.20151812
  CLC： E624
- Online First：12 January 2016，
  
  Published：12 January 2016
- 稿件说明：
移动端阅览
WANG Yumei, CHEN Hui, QIAN Feng. Improved biogeography-based optimization algorithm and its application in gasoline blending scheduling[J]. CIESC Journal, 2016, (3): 773-778.
DOI：

WANG Yumei, CHEN Hui, QIAN Feng. Improved biogeography-based optimization algorithm and its application in gasoline blending scheduling[J]. CIESC Journal, 2016, (3): 773-778. DOI： 10.11949/j.issn.0438-1157.20151812.

摘要

汽油调合和调度优化问题中含有典型的非线性约束(NLP)问题。针对一般智能优化算法在解决此类优化问题中易陷于局部极值

提出了一种改进的生物地理学优化算法(HMBBO)。该算法设计了一种基于种群个体差异信息的启发式变异算子

弥补了Gauss变异、Cauchy变异算子缺乏启发式信息的不足

以解决原算法在局部搜索时易出现的早熟问题

提高算法的全局搜索能力

并且采用非线性物种迁移模型以适应不同的自然环境。采用4个测试函数进行仿真

结果表明:HMBBO算法与标准BBO算法、基于Gauss变异及基于Cauchy变异的BBO算法比较

其收敛速度和全局寻优能力有明显改善。汽油调合和调度优化实例表明

该算法能够快速有效地找到全局最优解。

Abstract

The biogeography-based optimization (BBO) is a new swarm intelligence algorithm. To improve the global searching ability

solve the prematurity of BBO

a heuristic mutation operator is designed

which based on the differential information among the population individuals. It makes up the lack of the heuristic information on Gauss

Cauchy mutation operators. And the nonlinear migration model was introduced to the BBO considering to the natural environment. Tests are carried out through four standard test functions on the standard BBO

GMBBO

CMBBO and HMBBO independently

the results shows that HMBBO has a preferable convergence rate and search accuracy. The application of gasoline blending scheduling shows that HMBBO is effective.

关键词

Keywords

references

Views

135

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

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

Water quality prediction in wastewater treatment based on data decomposition and dung beetle optimized TCN-BiGRU/BiLSTM

Prediction of COD concentration in wastewater treatment plant effluent based on secondary decomposition and BiLSTM

A prediction method for effluent ammonia nitrogen concentration based on convolutional layer and attention mechanism long short-term memory network

ALNS-TS based fast optimization algorithm for large-scale maintenance task scheduling

Related Author

WANG Dantong

GAO Xiaoyong

ZHANG Li

FENG Xugang

TANG Lei

AN Shuo

YANG Ke

WANG Lu

Related Institution

Department of Automation, China University of Petroleum

Hainan Institute of China University of Petroleum

School of Electrical and Information Engineering, Anhui University of Technology, Ma’anshan

Anhui Province Key Laboratory of Special Heavy Load Robot, Ma’anshan

Engineering Research Institute, Anhui University of Technology, Ma’anshan

⁰