A structural analysis and design approach based on cellular computation models for analysis and optimization

A structural analysis and design approach based on cellular computation models for analysis and optimization

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Article ID: iaor20081975
Country: United Kingdom
Volume: 39
Issue: 4
Start Page Number: 381
End Page Number: 396
Publication Date: Jun 2007
Journal: Engineering Optimization
Authors: ,
Keywords: computational analysis: parallel computers
Abstract:

Genetic algorithms (GAs) have received considerable recent attention in problems of design optimization. The mechanics of population-based search in GAs are highly amenable to implementation on parallel computers. The present article describes a fine-grained model of parallel GA implementation that derives from a cellular-automata-like computation. The central idea behind the cellular genetic algorithm (CGA) approach is to treat the GA population as being distributed over a 2-D grid of cells, with each member of the population occupying a particular cell and defining the state of that cell. Evolution of the cell state is tantamount to updating the design information contained in a cell site and, as in cellular automata computations, takes place on the basis of local interaction with neighbouring cells. A special focus of the article is in the use of cellular automata (CA)-based models for structural analysis in conjunction with the CGA approach to optimization. In such an approach, the analysis and optimization are evolved simultaneously in a unified cellular computational framework. The article describes the implementation of this approach and examines its efficiency in the context of representative structural optimization problems.

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