Statistical Models Based Algorithms

Panos M. Pardalos, Antanas Žilinskas and Julius Žilinskas
Additional contact information
Panos M. Pardalos: University of Florida
Antanas Žilinskas: Vilnius University
Julius Žilinskas: Vilnius University

Chapter Chapter 7 in Non-Convex Multi-Objective Optimization, 2017, pp 97-120 from Springer

Abstract: Abstract Multi-objective optimization problems with expensive objective functions expensive function are typical in engineering design, where time-consuming computations are involved for modeling technological processes. Frequently, the available software implements an algorithm to compute the values of objective functions, but neither details of implementation nor analytical properties of the objective functions are known. Nevertheless, the continuity of the objective functions can be normally assumed. The complexity of the computational model implies not only the expensiveness of the objective function but also the uncertainty in its properties, so that other analytical properties of f(x), besides the continuity, cannot be substantiated. Such unfavorable, from the optimization point of view, properties of f(x) as non-differentiability, non-convexity, and multimodality cannot be excluded. Difficulties of the black-box black-box global optimization of expensive functions are well known from the experience gained in the single-objective case.

Keywords: Feasible Objective Region; Pareto Front; Single-objective Global Optimization; Current Optimization Step; Uniform Random Search (RUS) (search for similar items in EconPapers)
Date: 2017
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-319-61007-8_7

Ordering information: This item can be ordered from
http://www.springer.com/9783319610078

DOI: 10.1007/978-3-319-61007-8_7

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().