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

Erkan Gunpinar, Shahroz Khan

Research output: Contribution to journalArticle

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.
Original languageEnglish
JournalOptimization and Engineering
Early online date29 Nov 2019
DOIs
Publication statusE-pub ahead of print - 29 Nov 2019

Keywords

  • computer-aided design (CAD)
  • genetic algorithm (GA)
  • multi-objective optimization
  • mating selection

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