Fuzzy heuristics for sequential linear programming

Eric L. Mulkay, Singiresu S. Rao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

Numerical implementations of optimization algorithms often use parameters whose values are not strictly determined by the derivation of the algorithm, but must fall in some appropriate range of values. This work describes how fuzzy logic can be used to control such parameters to improve algorithms performance. This concept is shown with the use of sequential linear programming (SLP) due to its simplicity in implementation. The algorithm presented in this paper implements heuristics to improve the behavior of SLP based on current iterate values of design constraints and changes in search direction. Fuzzy logic is used to implement the heuristics in a form similar to what a human observer would do. An efficient algorithm, known as the infeasible primal-dual path-following interior-point method, is used for solving the sequence of LP problems. Four numerical examples are presented to show that the proposed SLP algorithm consistently performs better than the standard SLP algorithm.

Original languageEnglish (US)
Title of host publication23rd Design Automation Conference
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791880449
DOIs
StatePublished - 1997
Externally publishedYes
EventASME 1997 Design Engineering Technical Conferences, DETC 1997 - Sacramento, United States
Duration: Sep 14 1997Sep 17 1997

Publication series

NameProceedings of the ASME Design Engineering Technical Conference
Volume2

Conference

ConferenceASME 1997 Design Engineering Technical Conferences, DETC 1997
Country/TerritoryUnited States
CitySacramento
Period9/14/979/17/97

Keywords

  • Fuzzy control
  • Fuzzy heuristics
  • Optimization
  • Parameter adaptation
  • Sequential linear programming
  • Slp

ASJC Scopus subject areas

  • Mechanical Engineering
  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Modeling and Simulation

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