Bi-objective decision making in global optimization based on statistical models

Antanas Žilinskas, James Calvin

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

A global optimization problem is considered where the objective functions are assumed “black box” and “expensive”. An algorithm is theoretically substantiated using a statistical model of objective functions and the theory of rational decision making under uncertainty. The search process is defined as a sequence of bi-objective selections of sites for the computation of the objective function values. It is shown that two well known (the maximum average improvement, and the maximum improvement probability) algorithms are special cases of the proposed general approach.

Original languageEnglish (US)
Pages (from-to)599-609
Number of pages11
JournalJournal of Global Optimization
Volume74
Issue number4
DOIs
StatePublished - Aug 15 2019

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Management Science and Operations Research
  • Control and Optimization
  • Applied Mathematics

Keywords

  • Bi-objective decision making
  • Global optimization
  • Kriging
  • Rational decision making under uncertainty
  • Statistical models

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