Structural shape optimization by IGABEM and particle swarm optimization algorithm. (March 2018)

• Record Type: Journal Article
• Authors: Sun, S.H.; Yu, T.T.; Nguyen, T.T.; Atroshchenko, E.; Bui, T.Q.
• Is Part Of: Engineering analysis with boundary elements. Volume 88(2018)
• Page Start: 26
• Page End: 40
• Publication Date: 2018-03
• Subjects: Shape optimization -- NURBS -- Isogeometric analysis -- Boundary element method -- Particle swarm optimization algorithm; Boundary element methods -- Periodicals; Engineering mathematics -- Periodicals; Équations intégrales de frontière, Méthodes des -- Périodiques; Mathématiques de l'ingénieur -- Périodiques; Boundary element methods; Engineering mathematics; Periodicals; 620.00151
• DOI: 10.1016/j.enganabound.2017.12.007
• Languages: English
• ISSNs: 0955-7997

Abstract

Highlights: An effective approach combination of IGABEM with particle swarm optimization algorithm is developed for structural shape optimization. Geometry data can be taken directly from CAD. The need of domain parameterization is avoided. The optimized boundary is smooth. The sensitivity analysis is avoided compared with the gradient-based methods. Abstract: In this paper, a new approach is developed for structural shape optimization, which consists in coupling the particle swarm optimization (PSO) algorithm and the isogeometric boundary element method (IGA-BEM). The IGA-BEM is based on the combination of the isogeometric analysis (IGA) and the boundary element method (BEM), where Non-Uniform Rational B-Splines (NURBS) are employed as shape functions for geometry parameterization and approximation of the field variables. The method inherits the main advantage of the IGA-based shape optimization, i.e., the control points are used as design variables, thus the design model, analysis model and optimization model are uniformly described with the NURBS, providing easy communication between the three models and resulting in a smooth optimized boundary. However, the main feature of the proposed method is the use of PSO, which provides an attractive gradient-free alternative to complicated sensitivity analysis. The efficiency and accuracy of the proposed approach are demonstrated through four 2D shape optimization examples.