A multi-criteria based selection method using non-dominated sorting for genetic algorithm based design

Abstract

The paper presents a generative design approach, particularly for simulation-driven designs, using a genetic algorithm (GA), which is structured based on a novel offspring selection strategy. The proposed selection approach commences while enumerating the offsprings generated from the selected parents. Afterwards, a set of eminent offsprings is selected from the enumerated ones based on the following merit criteria: space-fillingness to generate as many distinct offsprings as possible, resemblance/non-resemblance of offsprings to the good/bad individuals, non-collapsingness to produce diverse simulation results and constrain-handling for the selection of offsprings satisfying design constraints. The selection problem itself is formulated as a multi-objective optimization problem. A greedy technique is employed based on non-dominated sorting, pruning, and selecting the representative solution. According to the experiments performed using three different application scenarios, namely simulation-driven product design, mechanical design and user-centred product design, the proposed selection technique outperforms the baseline GA selection techniques, such as tournament and ranking selections.

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(Images taken from Usta and Onder 2017)

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(Images taken from Gunpinar and Gunpinar 2018)

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Notes

  1. 1.

    See https://github.com/GMOnder01/Implant-Optimization

  2. 2.

    See https://github.com/shahrozkhan66/Sprint_Race_Car_Chassis_Analysis

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Acknowledgements

The authors would like to thank The Scientific and Technological Research Council of Turkey for supporting this research (Project No. 315M077), and Veysel Mert Usta and Gani Melik Onder to perform FEM tests for the dental implant models.

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Correspondence to Erkan Gunpinar.

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This study was funded by The Scientific and Technological Research Council of Turkey (Project No. 315M077).

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Gunpinar, E., Khan, S. A multi-criteria based selection method using non-dominated sorting for genetic algorithm based design. Optim Eng 21, 1319–1357 (2020). https://doi.org/10.1007/s11081-019-09477-8

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Keywords

  • Computer-aided design
  • Genetic algorithm
  • Mating selection
  • Multi-objective optimization