TITLE:
Topological Design via a Rule Based Genetic Optimization Algorithm
AUTHORS:
David Webb, Qian Liu, Wissam Alobaidi, Eric Sandgren
KEYWORDS:
Topological Design, Structural Optimization, Genetic Optimization, Variable Material Design
JOURNAL NAME:
American Journal of Computational Mathematics,
Vol.7 No.3,
August
28,
2017
ABSTRACT: A topological structural
design approach is presented which is based upon the implementation of a two
phase evolutionary optimization algorithm in conjunction with a finite element
analysis code. The first phase utilizes a conventional genetic approach which performs a global search for the
optimal design topology. Dual level material properties are specified within
the genetic encoding and are applied to each individual element in the design
mesh to represent either design material or a void. The second phase introduces a rule
based refinement which allows for user design intent to accelerate the solution
process and eliminate obvious design discrepancies resulting from the phase one
search. A series of plate design problems are presented where the objective is
to minimize the overall volume of the structure under predefined loading and
constraint conditions. The constraints include both stress and deflection
considerations where stress is calculated through the use of a commercial
finite element package. The initial plate example incorporates a coarse mesh,
but a gradual decrease in element size was employed for the remaining cases
examined. Replacement of the phase one search with a set of randomly generated
designs is demonstrated in order to form a greatly reduced design space which
drastically increases the efficiency of the solution process. Comparison
results are drawn between the conventional genetic algorithm and the two phase
procedure.