CLC number: TQ150.9; O646.5; X783
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 0000-00-00
Cited: 17
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GUO Chuang-xin, HU Jia-sheng, YE Bin, CAO Yi-jia. Swarm intelligence for mixed-variable design optimization[J]. Journal of Zhejiang University Science A, 2004, 5(7): 851-860.
@article{title="Swarm intelligence for mixed-variable design optimization",
author="GUO Chuang-xin, HU Jia-sheng, YE Bin, CAO Yi-jia",
journal="Journal of Zhejiang University Science A",
volume="5",
number="7",
pages="851-860",
year="2004",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2004.0851"
}
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%T Swarm intelligence for mixed-variable design optimization
%A GUO Chuang-xin
%A HU Jia-sheng
%A YE Bin
%A CAO Yi-jia
%J Journal of Zhejiang University SCIENCE A
%V 5
%N 7
%P 851-860
%@ 1869-1951
%D 2004
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2004.0851
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T1 - Swarm intelligence for mixed-variable design optimization
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A1 - HU Jia-sheng
A1 - YE Bin
A1 - CAO Yi-jia
J0 - Journal of Zhejiang University Science A
VL - 5
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SP - 851
EP - 860
%@ 1869-1951
Y1 - 2004
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2004.0851
Abstract: Many engineering optimization problems frequently encounter continuous variables and discrete variables which adds considerably to the solution complexity. Very few of the existing methods can yield a globally optimal solution when the objective functions are non-convex and non-differentiable. This paper presents a hybrid swarm intelligence approach (HSIA) for solving these nonlinear optimization problems which contain integer, discrete, zero-one and continuous variables. HSIA provides an improvement in global search reliability in a mixed-variable space and converges steadily to a good solution. An approach to handle various kinds of variables and constraints is discussed. Comparison testing of several examples of mixed-variable optimization problems in the literature showed that the proposed approach is superior to current methods for finding the best solution, in terms of both solution quality and algorithm robustness.
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