Framework for sequential approximate optimization

作     者：Jacobs, JH Etman, LFP van Keulen, F Rooda, JE 

作者机构：Eindhoven Univ Technol Dept Mech Engn Syst Engn Grp NL-5600 MB Eindhoven Netherlands Fac Design Engn & Prod Struct Optimizat & Computat Mech Grp NL-2628 CD Delft Netherlands 

出 版 物：《STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION》 

年 卷 期：2004年第27卷第5期

页      面：384-400页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0801[工学-力学（可授工学、理学学位）] 080102[工学-固体力学] 

主　　题：object-oriented software framework Python sequential approximate optimization 

摘      要：An object-oriented framework for sequential approximate optimization (SAO) is proposed. The framework aims to provide an open environment for the specification and implementation of SAO strategies. The framework is based on the Python programming language and contains a toolbox of Python classes, methods, and interfaces to external software. The framework distinguishes modules related to the optimization problem, the SAO sequence, and the numerical routines used in the SAO approach. The problem-related modules specify the optimization problem, including the simulation model for the evaluation of the objective function and constraints. The sequence-related modules specify the sequence of SAO steps. The routine-related modules represent numerical routines used in the SAO steps as black-box functions with predefined input and output, e.g., from external software libraries. The framework enables the user to (re-) specify or extend the SAO dependent modules, which is generally impossible in most available SAO implementations. This is highly advantageous since many SAO approaches are application-domain specific due to the type of approximation functions used. A ten-bar truss design problem with fixed loads as well as uncertain loads is used as an illustration and demonstrates the flexibility of the framework.