Elite and dynamic opposite learning enhanced sine cosine algorithm for application to plat-fin heat exchangers design problem

被引：18

作者：

Zhang, Lidong ^{[1
]}

Hu, Tianyu ^{[1
]}

Yang, Zhile ^{[2
]}

Yang, Dongsheng ^{[3
]}

Zhang, Jianhua ^{[4
]}

机构：

[1] Northeast Elect Power Univ, Jilin 132012, Jilin, Peoples R China

[2] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China

[3] Northeastern Univ, Intelligent Elect Sci & Technol Res Inst, Shenyang 110819, Peoples R China

[4] North China Elect Power Univ, State Key Lab Alternate Elect Power Syst Renewabl, Beijing 102206, Peoples R China

来源：

NEURAL COMPUTING & APPLICATIONS | 2023年 / 35卷 / 17期

基金：

美国国家科学基金会;

关键词：

Plate-fin heat exchanger; Design optimization; Sine cosine algorithm; Dynamic-opposite learning; ECONOMIC OPTIMIZATION; GLOBAL OPTIMIZATION; DIFFERENTIAL EVOLUTION; GENETIC ALGORITHM; SYSTEM; SWARM;

D O I：

10.1007/s00521-021-05963-2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The heat exchanger has been widely used in the energy and chemical industry and plays an irreplaceable role in the featured applications. The design of heat exchanger is a mixed integer complex optimization problem, where the efficient design significantly improves the efficiency and reduces the cost. Many intelligent methods have been developed for heat exchanger optimal design. In this paper, a novel variant of sine and cosine algorithm named EDOLSCA is proposed, enhanced by dynamic opposite learning algorithm and the elite strategy. The proposed method is tested in CEC2014 benchmark and proved to be of significant advantages over the original algorithm. The new algorithm is then validated in the plate-fin heat exchanger (PFHE) optimal design problem. The comparison results of the proposed algorithm and other algorithms prove that EDOLSCA also has demonstrated superiority in heat exchanger optimal design.

引用

页码：12401 / 12414

页数：14

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