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dc.contributor.authorPenninger, Charles L.en_US
dc.contributor.authorTovar, Andresen_US
dc.contributor.authorWatson, Layne T.en_US
dc.contributor.authorRenaud, John E.en_US
dc.date.accessioned2013-06-19T14:36:10Z
dc.date.available2013-06-19T14:36:10Z
dc.date.issued2009
dc.identifierhttp://eprints.cs.vt.edu/archive/00001084/en_US
dc.identifier.urihttp://hdl.handle.net/10919/20239
dc.description.abstractThe hybrid cellular automaton (HCA) method is a biologically inspired algorithm capable of topology synthesis that was developed to simulate the behavior of the bone functional adaptation process. In this algorithm, the design domain is divided into cells with some communication property among neighbors. Local evolutionary rules, obtained from classical control theory, iteratively establish the value of the design variables in order to minimize the local error between a field variable and a corresponding target value. Karush-Kuhn-Tucker (KKT) optimality conditions have been derived to determine the expression for the field variable and its target. While averaging techniques mimicking intercellular communication have been used to mitigate numerical instabilities such as checkerboard patterns and mesh dependency, some questions have been raised whether KKT conditions are fully satisfied in the final topologies. Furthermore, the averaging procedure might result in cancellation or attenuation of the error between the field variable and its target. Several examples are presented showing that HCA converges to different final designs for different neighborhood configurations or averaging schemes. Although it has been claimed that these final designs are optimal, this might not be true in a precise mathematical sense—the use of the averaging procedure induces a mathematical incorrectness that has to be addressed. In this work, a new adaptive neighboring scheme will be employed that utilizes a weighting function for the influence of a cell’s neighbors that decreases to zero over time. When the weighting function reaches zero, the algorithm satisfies the aforementioned optimality criterion. Thus, the HCA algorithm will retain the benefits that result from utilizing neighborhood information, as well as obtain an optimal solution.en_US
dc.format.mimetypeapplication/pdfen_US
dc.publisherDepartment of Computer Science, Virginia Polytechnic Institute & State Universityen_US
dc.subjectAlgorithmsen_US
dc.subjectData structuresen_US
dc.titleKKT conditions satisfied using adaptive neighboring in hybrid cellular automata for topology optimizationen_US
dc.typeTechnical reporten_US
dc.identifier.trnumberTR-09-18en_US
dc.type.dcmitypeTexten_US
dc.identifier.sourceurlhttp://eprints.cs.vt.edu/archive/00001084/01/hcaJCISE09.pdf


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