改进的膜计算仿生优化算法及在汽油调和中的应用

赵进慧; 柴天佑; 周平

doi:10.3969/j.issn.0438-1157.2012.09.047

您当前的位置：

首页 >

文章列表页 >

改进的膜计算仿生优化算法及在汽油调和中的应用

过程系统工程 | 更新时间：2025-12-10

- 改进的膜计算仿生优化算法及在汽油调和中的应用
- Modified bio-inspired algorithm based on membrane computing and application in gasoline blending
- 化工学报 2012年第9期页码：2965-2971
- 作者机构：
  
  东北大学流程工业综合自动化国家重点实验室,辽宁,沈阳,110819
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0438-1157.2012.09.047
  中图分类号： P18
- 网络首发：2012-09-05，
  
  纸质出版：2012-09-05
- 稿件说明：
移动端阅览
赵进慧, 柴天佑, 周平. 改进的膜计算仿生优化算法及在汽油调和中的应用[J]. 化工学报, 2012,(9):2965-2971.

ZHAO Jinhui, CHAI Tianyou, ZHOU Ping. Modified bio-inspired algorithm based on membrane computing and application in gasoline blending[J]. CIESC Journal, 2012, (9): 2965-2971.
赵进慧, 柴天佑, 周平. 改进的膜计算仿生优化算法及在汽油调和中的应用[J]. 化工学报, 2012,(9):2965-2971. DOI： 10.3969/j.issn.0438-1157.2012.09.047.

ZHAO Jinhui, CHAI Tianyou, ZHOU Ping. Modified bio-inspired algorithm based on membrane computing and application in gasoline blending[J]. CIESC Journal, 2012, (9): 2965-2971. DOI： 10.3969/j.issn.0438-1157.2012.09.047.

摘要

为提高膜计算仿生优化算法在求解流程工业复杂优化问题的计算性能

提出一种改进的膜计算仿生优化算法。该算法采用一个新的不确定性提取规则取代改进前的提取规则。4个有约束标准测试函数被用于检验该算法的计算性能

计算结果及对比显示了改进算法在鲁棒性和效率等方面优于改进前算法。改进算法应用于汽油调和优化问题

更高利润的配方及算法的计算效率证实了改进算法的优越性和实用性。

Abstract

Aiming at improving the computational performance of bio-inspired algorithm based on membrane computing(BIAMC)in solving complex optimization problems in process manufacturing

a modified bio-inspired algorithm based on membrane computing(MBIAMC)is proposed.In MBIAMC

a new indeterministic abstraction rule is applied which substitutes the abstraction rule of BIAMC

and the algorithmic framework and other rules are inherited from BIAMC.For solving constrained optimization problems

the quadratic penalty function method is introduced in MBIAMC.Four constrained benchmark functions are used to evaluate computational performance of MBIAMC.The results and comparison with other two algorithms handling constraints problems reveal that MBIAMC is efficiency and superiority to BIAMC in accuracy and robustness.As a case study

MBIAMC is used to solve gasoline blending optimization problem

the better recipes and its lower computational cost validate its higher efficiency and more practicability.

关键词

Keywords

references

浏览量

193

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于机器学习的改型歧管式微通道热沉流动传热特性多目标优化

固体蓄冷填充床性能提升及保冷特性研究

PMMA涂胶隔膜对软包锂离子电池制程及性能影响研究

煤基冷热电联供优化方法

基于深度强化学习的某异构生产线工厂生产智能排产