Details
| Original language | English |
|---|---|
| Pages (from-to) | 401-416 |
| Number of pages | 16 |
| Journal | Engineering Optimization |
| Volume | 49 |
| Issue number | 3 |
| Early online date | 22 Jun 2016 |
| Publication status | Published - Mar 2017 |
Abstract
The synthesis of heat exchanger networks (HENs) is a complex problem because of the nonlinearity that results from the integer and continuous variables. Here, a bi-level algorithm for the optimal design of a HEN is proposed that attempts to optimize separately the integer and continuous variables on two levels. The master level is a problem-oriented evolution method generating new candidate HEN structures. The slave level is a memetic particle swarm optimization, an improved particle swarm optimization combined with a local search component, improvement of neighbourhood topologies and control parameter preference. The slave level minimizes the total annual cost (TAC) of a given structure received from the master level, and then sends this value back to the master level for structure evolution. The proposed bi-level method is applied to several cases taken from the literature, which demonstrate its reliable search ability in both structure space and continuous variable space and its ability to optimize the system, producing generally lower TACs than previously used methods.
Keywords
- bi-level algorithm, evolution method, local search, MPSO, synthesis of HENs
ASJC Scopus subject areas
- Computer Science(all)
- Computer Science Applications
- Mathematics(all)
- Control and Optimization
- Decision Sciences(all)
- Management Science and Operations Research
- Engineering(all)
- Industrial and Manufacturing Engineering
- Mathematics(all)
- Applied Mathematics
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In: Engineering Optimization, Vol. 49, No. 3, 03.2017, p. 401-416.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Bi-level heat exchanger network synthesis with evolution method for structure optimization and memetic particle swarm optimization for parameter optimization
AU - Wang, Jinyang
AU - Cui, Guomin
AU - Xiao, Yuan
AU - Luo, Xing
AU - Kabelac, Stephan
N1 - Funding Information: The present research project was support by the Natural Science Foundation of China [grant no. 51176125]. Publisher Copyright: © 2016 Informa UK Limited, trading as Taylor & Francis Group. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/3
Y1 - 2017/3
N2 - The synthesis of heat exchanger networks (HENs) is a complex problem because of the nonlinearity that results from the integer and continuous variables. Here, a bi-level algorithm for the optimal design of a HEN is proposed that attempts to optimize separately the integer and continuous variables on two levels. The master level is a problem-oriented evolution method generating new candidate HEN structures. The slave level is a memetic particle swarm optimization, an improved particle swarm optimization combined with a local search component, improvement of neighbourhood topologies and control parameter preference. The slave level minimizes the total annual cost (TAC) of a given structure received from the master level, and then sends this value back to the master level for structure evolution. The proposed bi-level method is applied to several cases taken from the literature, which demonstrate its reliable search ability in both structure space and continuous variable space and its ability to optimize the system, producing generally lower TACs than previously used methods.
AB - The synthesis of heat exchanger networks (HENs) is a complex problem because of the nonlinearity that results from the integer and continuous variables. Here, a bi-level algorithm for the optimal design of a HEN is proposed that attempts to optimize separately the integer and continuous variables on two levels. The master level is a problem-oriented evolution method generating new candidate HEN structures. The slave level is a memetic particle swarm optimization, an improved particle swarm optimization combined with a local search component, improvement of neighbourhood topologies and control parameter preference. The slave level minimizes the total annual cost (TAC) of a given structure received from the master level, and then sends this value back to the master level for structure evolution. The proposed bi-level method is applied to several cases taken from the literature, which demonstrate its reliable search ability in both structure space and continuous variable space and its ability to optimize the system, producing generally lower TACs than previously used methods.
KW - bi-level algorithm
KW - evolution method
KW - local search
KW - MPSO
KW - synthesis of HENs
UR - http://www.scopus.com/inward/record.url?scp=84975499172&partnerID=8YFLogxK
U2 - 10.1080/0305215X.2016.1191803
DO - 10.1080/0305215X.2016.1191803
M3 - Article
AN - SCOPUS:84975499172
VL - 49
SP - 401
EP - 416
JO - Engineering Optimization
JF - Engineering Optimization
SN - 0305-215X
IS - 3
ER -