Fuzzy nonlinear programming for mixed-discrete design optimization through hybrid genetic algorithm

Ying Xiong, Singiresu S. Rao

Research output: Contribution to journalArticlepeer-review

29 Scopus citations


Many practical engineering optimization problems involve discrete or integer design variables, and often the design decisions are to be made in a fuzzy environment in which the statements might be vague or imprecise. A mixed-discrete fuzzy nonlinear programming approach that combines the fuzzy λ-formulation with a hybrid genetic algorithm is proposed in this paper. This method can find a globally compromise solution for a mixed-discrete fuzzy optimization problem, even when the objective function is nonconvex and nondifferentiable. In the construction of the objective membership function, an error from the early research work is corrected and the right conclusion has been made. The illustrative examples demonstrate that more reliable and satisfactory results can be obtained through the present method.

Original languageEnglish (US)
Pages (from-to)167-186
Number of pages20
JournalFuzzy Sets and Systems
Issue number2
StatePublished - Sep 1 2004


  • Engineering design
  • Fuzzy programming
  • Hybrid genetic algorithm
  • Membership function
  • Mixed-discrete optimization

ASJC Scopus subject areas

  • Statistics and Probability
  • Electrical and Electronic Engineering
  • Statistics, Probability and Uncertainty
  • Information Systems and Management
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Artificial Intelligence


Dive into the research topics of 'Fuzzy nonlinear programming for mixed-discrete design optimization through hybrid genetic algorithm'. Together they form a unique fingerprint.

Cite this