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

Antanas Žilinskas; James Calvin

doi:10.1007/s10898-018-0622-5

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

Antanas Žilinskas, James Calvin

Research output: Contribution to journal › Article › peer-review

26 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 language	English (US)
Pages (from-to)	599-609
Number of pages	11
Journal	Journal of Global Optimization
Volume	74
Issue number	4
DOIs	https://doi.org/10.1007/s10898-018-0622-5
State	Published - Aug 15 2019
Externally published	Yes

All Science Journal Classification (ASJC) codes

Control and Optimization
Applied Mathematics
Business, Management and Accounting (miscellaneous)
Computer Science Applications
Management Science and Operations Research

Keywords

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

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10.1007/s10898-018-0622-5

Cite this

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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.",

keywords = "Bi-objective decision making, Global optimization, Kriging, Rational decision making under uncertainty, Statistical models",

author = "Antanas {\v Z}ilinskas and James Calvin",

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AU - Calvin, James

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AB - 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.

KW - Bi-objective decision making

KW - Global optimization

KW - Kriging

KW - Rational decision making under uncertainty

KW - Statistical models

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

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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