A Hybrid Algorithm Based on Differential Evolution and Group Search Optimization and Its Application on Ethylene Cracking Furnace

年笑宇; 王振雷; 钱锋

doi:10.1016/S1004-9541(13)60531-5

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A Hybrid Algorithm Based on Differential Evolution and Group Search Optimization and Its Application on Ethylene Cracking Furnace

过程系统工程与过程安全 | Updated：2026-01-06

- A Hybrid Algorithm Based on Differential Evolution and Group Search Optimization and Its Application on Ethylene Cracking Furnace
- A Hybrid Algorithm Based on Differential Evolution and Group Search Optimization and Its Application on Ethylene Cracking Furnace
- 中国化学工程学报（英文版） 2013年21卷第5期页码：537-5431004-954160531-5
- Affiliations：
  
  Key Laboratory of Advanced Control and Optimization for Chemical Processes (Ministry of Education) East China University of Science and Technology,Shanghai,China,200237
- Author bio：
- Funds：
- DOI：10.1016/S1004-9541(13)60531-5
  中图分类号：
- 纸质出版：2013
- Accepted：
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年笑宇, 王振雷, 钱锋. A Hybrid Algorithm Based on Differential Evolution and Group Search Optimization and Its Application on Ethylene Cracking Furnace[J]. 中国化学工程学报（英文版）, 2013,21(5):537-5431004-954160531-5.

NIAN Xiaoyu, WANG Zhenlei, QIAN Feng. A Hybrid Algorithm Based on Differential Evolution and Group Search Optimization and Its Application on Ethylene Cracking Furnace[J]. Chin.J.Chem.Eng., 2013, 21(5): 537-543.
年笑宇, 王振雷, 钱锋. A Hybrid Algorithm Based on Differential Evolution and Group Search Optimization and Its Application on Ethylene Cracking Furnace[J]. 中国化学工程学报（英文版）, 2013,21(5):537-5431004-954160531-5. DOI： 10.1016/S1004-9541(13)60531-5.

NIAN Xiaoyu, WANG Zhenlei, QIAN Feng. A Hybrid Algorithm Based on Differential Evolution and Group Search Optimization and Its Application on Ethylene Cracking Furnace[J]. Chin.J.Chem.Eng., 2013, 21(5): 537-543. DOI： 10.1016/S1004-9541(13)60531-5.

摘要

Abstract

To find the optimal operational condition when the properties of feedstock changes in the cracking furnace online

a hybrid algorithm named differential evolution group search optimization (DEGSO) is proposed

which is based on the differential evolution (DE) and the group search optimization (GSO). The DEGSO combines the advantages of the two algorithms: the high computing speed of DE and the good performance of the GSO for preventing the best particle from converging to local optimum. A cooperative method is also proposed for switching between these two algorithms. If the fitness value of one algorithm keeps invariant in several generations and less than the preset threshold

it is considered to fall into the local optimization and the other algorithm is chosen. Experiments on benchmark functions show that the hybrid algorithm outperforms GSO in accuracy

global searching ability and efficiency. The optimization of ethylene and propylene yields is illustrated as a case by DEGSO. After optimization

the yield of ethylene and propylene is increased remarkably

which provides the proper operational condition of the ethylene cracking furnace.

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