Intelligent Computing in Engineering and Architecture

Smith, Ian F. C.

doi:10.1007/11888598

Cited by 6 publications

(2 citation statements)

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“…Rather than relying on the limited design freedom associated with performance-to-design space mappings, this work builds upon the modeling to generate alternatives (MGA) approach [17][18][19][65][66][67]. MGA identifies design alternatives that maximize design space distance from an initial point while satisfying designer-defined performance loss constraints.…”

Section: Introductionmentioning

confidence: 99%

“…Kripakaran and Gupta, for example, used the HSJ variation to optimize a Moment-Resisting Steel Frame [66], where their goal was to identify locations that would minimize the cost of the structure while maintaining the necessary strength to resist wind loading. More recent applications of MGA include the use of genetic algorithms to help solve watershed development problems [67] and an application of MGA to energy modeling [19].…”

Section: Introductionmentioning

confidence: 99%

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Exploring the Effectiveness of Using Graveyard Data When Generating Design Alternatives

Foster¹,

Ferguson

2013

Journal of Computing and Information Science in Engineering

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The objective of this paper is to demonstrate that unique alternative designs can be efficiently found by searching the discarded data (or graveyard) from a multiobjective genetic algorithm (MOGA). Motivation for using graveyard data to generate design alternatives arises from the computational cost associated with real-time design space exploration of multiobjective optimization problems. The effectiveness of this approach is explored by comparing (1) the uniqueness of alternatives found using graveyard data and those generated using an optimization-based search, and (2) how alternative generation near the Pareto frontier is impacted. Two multiobjective case study problems are introduced-a two bar truss and an I-beam design optimization. Results from these studies indicate that using graveyard data allows for the discovery of alternative designs that are at least 70% as unique as alternatives found using an optimization-based alternative identification approach, while saving a significant number of functional evaluations. Additionally, graveyard data are shown to be better suited for alternative generation near the Pareto frontier than standard sampling techniques. Finally, areas of future work are also discussed.

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