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Title:Large scale structural optimization using genetic and generative algorithms with sequential linear programming
Author(s):Khetan, Ashish Kumar
Advisor(s):Allison, James T.
Department / Program:Industrial&Enterprise Sys Eng
Discipline:Industrial Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Truss Optimization
Generative Algorithms
Topology Optimization
Abstract:This thesis explores novel parameterization concepts for large scale topology optimization that enables the use of evolutionary algorithms in large-scale structural design. Specifically, two novel parameterization concepts based on generative algorithms and Boolean random networks are proposed that facilitate systematic exploration of the design space while limiting the number of design variables. The presented methodology is demonstrated on classical planar and space truss optimization problems. A nested optimization methodology using genetic algorithms and sequential linear programming is also proposed to solve truss optimization problems. Further, a number of heuristics are also presented to perform the parameterization efficiently. The results obtained on solving the standard truss optimization problems are very encouraging.
Issue Date:2014-05-30
Rights Information:Copyright 2014 Ashish Kumar Khetan
Date Available in IDEALS:2014-05-30
Date Deposited:2014-05

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