 Random Search Procedures for Global OptimizationKurt Marti
Chapter Chapter 6 in Optimization Under Stochastic Uncertainty, 2020, pp 123-138 from Springer Abstract: Abstract Solving optimization problems from engineering, as, e.g., parameter—or process—optimization problems min F ( x ) s.t. x ∈ D , $$\displaystyle \min F (x) \mbox{ s.t. } x \in D, $$ where D is a subset of ℝ n $$\mathbb {R}^n$$ , one meets often the following situation: (a) One should find the global optimum in (6.1), hence most of the deterministic programming procedures, which are based on local improvements of the performance index F(x), will fail. (b) Concerning the objective function F one has a blackbox—situation, i.e. there is only few a priori information A priori information about the structure of F, especially there is no knowledge about the direct functional relationship between the control or input vector x ∈ D and its index of performance F(x); hence—besides the more or less detailed a priori information about F—the only way of getting objective information about the structure of F is via evaluations of its values F(x) by experiments or by means of a numerical procedure simulating the technical plant. Date: 2020 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-030-55662-4_6 Ordering information: This item can be ordered from DOI: 10.1007/978-3-030-55662-4_6 Access Statistics for this chapter More chapters in International Series in Operations Research & Management Science from Springer |
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