Shape optimization using reproducing kernel particle method and an enriched genetic algorithm

Z. Q. Zhang; J. X. Zhou; N. Zhou; X. M. Wang; L. Zhang

doi:10.1016/j.cma.2004.10.004

Shape optimization using reproducing kernel particle method and an enriched genetic algorithm

Z. Q. Zhang
, J. X. Zhou
, N. Zhou
, X. M. Wang
, L. Zhang

School of Aerospace Engineering

Xi'an Jiaotong University

Research output: Contribution to journal › Article › peer-review

30 Scopus citations

Abstract

Combining Reproducing Kernel Particle Method (RKPM) with the proposed Multi-Family Genetic Algorithm (MFGA), a novel approach to continuum-based shape optimization problems is brought forward in this paper. Taking full advantage of the features of meshfree method and the merits of MFGA, the new method solves shape optimization problems in such a unique way that remeshing is avoided and particularly the computation burden and errors caused by sensitivity analysis are eliminated completely. The effectiveness, versatility and performance of the proposed approach are demonstrated via three 2-D numerical examples.

Original language	English
Pages (from-to)	4048-4070
Number of pages	23
Journal	Computer Methods in Applied Mechanics and Engineering
Volume	194
Issue number	39-41
DOIs	https://doi.org/10.1016/j.cma.2004.10.004
State	Published - 1 Oct 2005

Keywords

Genetic algorithms
Meshfree methods
Reproducing kernel particle method
Shape optimization

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10.1016/j.cma.2004.10.004

Cite this

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title = "Shape optimization using reproducing kernel particle method and an enriched genetic algorithm",

abstract = "Combining Reproducing Kernel Particle Method (RKPM) with the proposed Multi-Family Genetic Algorithm (MFGA), a novel approach to continuum-based shape optimization problems is brought forward in this paper. Taking full advantage of the features of meshfree method and the merits of MFGA, the new method solves shape optimization problems in such a unique way that remeshing is avoided and particularly the computation burden and errors caused by sensitivity analysis are eliminated completely. The effectiveness, versatility and performance of the proposed approach are demonstrated via three 2-D numerical examples.",

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T1 - Shape optimization using reproducing kernel particle method and an enriched genetic algorithm

AU - Zhang, Z. Q.

AU - Zhou, J. X.

AU - Zhou, N.

AU - Wang, X. M.

AU - Zhang, L.

PY - 2005/10/1

Y1 - 2005/10/1

N2 - Combining Reproducing Kernel Particle Method (RKPM) with the proposed Multi-Family Genetic Algorithm (MFGA), a novel approach to continuum-based shape optimization problems is brought forward in this paper. Taking full advantage of the features of meshfree method and the merits of MFGA, the new method solves shape optimization problems in such a unique way that remeshing is avoided and particularly the computation burden and errors caused by sensitivity analysis are eliminated completely. The effectiveness, versatility and performance of the proposed approach are demonstrated via three 2-D numerical examples.

AB - Combining Reproducing Kernel Particle Method (RKPM) with the proposed Multi-Family Genetic Algorithm (MFGA), a novel approach to continuum-based shape optimization problems is brought forward in this paper. Taking full advantage of the features of meshfree method and the merits of MFGA, the new method solves shape optimization problems in such a unique way that remeshing is avoided and particularly the computation burden and errors caused by sensitivity analysis are eliminated completely. The effectiveness, versatility and performance of the proposed approach are demonstrated via three 2-D numerical examples.

KW - Genetic algorithms

KW - Meshfree methods

KW - Reproducing kernel particle method

KW - Shape optimization

UR - https://www.scopus.com/pages/publications/21044452188

U2 - 10.1016/j.cma.2004.10.004

DO - 10.1016/j.cma.2004.10.004

M3 - 文章

AN - SCOPUS:21044452188

SN - 0045-7825

VL - 194

SP - 4048

EP - 4070

JO - Computer Methods in Applied Mechanics and Engineering

JF - Computer Methods in Applied Mechanics and Engineering

IS - 39-41

ER -

Shape optimization using reproducing kernel particle method and an enriched genetic algorithm

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Keywords

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