Applying neural network and scatter search to optimize parameter design with dynamic characteristics

Chao Ton Su, Mu-Chen Chen*, Hsiao Ling Chan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

33 Scopus citations


Parameter design is critical to enhancing a system's robustness by identifying specific control factor set points (levels) that make the system least sensitive to noise. Engineers have conventionally applied Taguchi methods to optimize parameter design. However, Taguchi methods can only obtain the optimal solution among the specified control factor levels. They cannot identify the real optimum when the parameter values are continuous. This study proposes a hybrid procedure combining neural networks and scatter search to optimize the continuous parameter design problem. First, neural networks are used to simulate the relationship between the control factor values and corresponding responses. Second, scatter search is employed to obtain the optimal parameter settings. The desirability function is utilized to transform the multiple responses into a single response. A case with dynamic characteristics is carried out in blood glucose strip manufacturing in Taiwan to demonstrate the practicability of the proposed procedure.

Original languageEnglish
Pages (from-to)1132-1140
Number of pages9
JournalJournal of the Operational Research Society
Issue number10
StatePublished - 1 Oct 2005


  • Dynamic characteristic
  • Multi-response
  • Neural network
  • Parameter design
  • Scatter search

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