Download PDF by Gabriele Eichfelder: Adaptive Scalarization Methods In Multiobjective

March 2, 2018 | Linear Programming | By admin | 0 Comments

By Gabriele Eichfelder

ISBN-10: 3540791574

ISBN-13: 9783540791577

This ebook offers adaptive answer tools for multiobjective optimization difficulties according to parameter based scalarization ways. With the aid of sensitivity effects an adaptive parameter keep an eye on is constructed such that top quality approximations of the effective set are generated. those examinations are according to a distinct scalarization procedure, however the software of those effects to many different recognized scalarization equipment is usually offered. Thereby very basic multiobjective optimization difficulties are thought of with an arbitrary partial ordering outlined through a closed pointed convex cone within the target house. The effectiveness of those new tools is validated with a number of try difficulties in addition to with a up to date challenge in intensity-modulated radiotherapy. The ebook concludes with an extra software: a process for fixing multiobjective bilevel optimization difficulties is given and is utilized to a bicriteria bilevel challenge in clinical engineering.

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Problem (SP(a, r)) is also discussed by Helbig in [104]. He interprets the point a as a reference point and the parameter r as a direction. For r ∈ int(Rm + ) this corresponds to the interpretation of r as a weighting of the objective functions with the weights wi := r1i , i = 1, . . 2 Properties of the Pascoletti-Serafini Scalarization 25 (compare with the weighted Chebyshev norm). 1,c)) which is closest to the reference point. The Pascoletti-Serafini problem is also related to a scalarization introduced by Gerstewitz in [91] as well as to the problem discussed in [92, 237] by Tammer, Weidner and Winkler.

R. t. 27. 25 hold and let K = Rm + , C = R+ , and Sˆ = S = Rn . A point x ¯ is a minimal solution of (Pk (ε)) with Lagrange multipliers μ ¯i ∈ R+ for i ∈ {1, . . , m} \ {k}, ν¯ ∈ Rp+ , and ξ¯ ∈ Rq , if and only if (fk (¯ x), x ¯) is a minimal solution of (SP(a, r)) with ¯ with μ Lagrange multipliers (¯ μ, ν¯, ξ) ¯k = 1, and ai = εi , ∀i ∈ {1, . . , m} \ {k}, with ek the kth unit vector in Rm . 25) 50 2 Scalarization Approaches Proof. By introducing the additional variable t ∈ R the scalar optimization problem (Pk (ε)) can be formulated as subject εi − fi (x) ≥ t − fk (x) ≥ ≥ gj (x) = hl (x) t ∈ R, x ∈ Rn .

For int(K) = ∅ we cannot apply the preceding theorem. However we can still consider the case of finding minimal points w. r. t. 11) icr(K). 503]). 3. We consider the scalar optimization problem (SP(a, r)) to the multiobjective optimization problem (MOP) with a ∈ Rm , r ∈ L(K). Let (t¯, x ¯) be a minimal solution, then x ¯ is minimal w. r. t. icr(K) ∪ {0m }. For the proof of this theorem we refer to [181]. 2 Properties of the Pascoletti-Serafini Scalarization 29 has no minimal solution, we can conclude under some additional assumptions that the related multiobjective optimization problem has no K-minimal solution at all.

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Adaptive Scalarization Methods In Multiobjective Optimization by Gabriele Eichfelder


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