Advancements in the computation of enclosures for multi-objective optimization problems
•Presents a framework to solve multi-objective optimization problems.•Suitable for convex and mixed-integer quadratic multi-objective optimization problems.•Generalizes the concept of Local Upper Bounds.•Demonstrates how to warm start computations of enclosures.•Includes computational results of alg...
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| Vydané v: | European journal of operational research Ročník 310; číslo 1; s. 315 - 327 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier B.V
01.10.2023
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| Predmet: | |
| ISSN: | 0377-2217, 1872-6860 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | •Presents a framework to solve multi-objective optimization problems.•Suitable for convex and mixed-integer quadratic multi-objective optimization problems.•Generalizes the concept of Local Upper Bounds.•Demonstrates how to warm start computations of enclosures.•Includes computational results of algorithmic framework for selected test instances.
A central goal for multi-objective optimization problems is to compute their nondominated sets. In most cases these sets consist of infinitely many points and it is not a practical approach to compute them exactly. One solution to overcome this problem is to compute an enclosure, a special kind of coverage, of the nondominated set. For that computation one often makes use of so-called local upper bounds. In this paper we present a generalization of this concept. For the first time, this allows to apply a warm start strategy to the computation of an enclosure. We also show how this generalized concept allows to remove empty areas of an enclosure by deleting certain parts of the lower and upper bound sets which has not been possible in the past. We demonstrate how to apply our ideas to the box approximation algorithm, a general framework to compute an enclosure, as recently used in the solver called BAMOP. We show how that framework can be simplified and improved significantly, especially concerning its practical numerical use. In fact, we show for selected numerical instances that our new approach is up to eight times faster than the original one. Hence, our new framework is not only of theoretical but also of practical use, for instance for continuous convex or mixed-integer quadratic optimization problems. |
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| ISSN: | 0377-2217 1872-6860 |
| DOI: | 10.1016/j.ejor.2023.02.032 |