Solving Multi-objective Pseudo-Boolean Problems

Lukasiewycz, Martin; Glaß, Michael; Haubelt, Christian; Teich, Jürgen

doi:10.1007/978-3-540-72788-0_9

Cited by 6 publications

(3 citation statements)

References 19 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the area of Boolean-based optimization procedures, there has been preliminary work on solving pseudo-Boolean MOCO problems [39]. Nevertheless, this work addresses exclusively Pareto optimality, and does not cover lexicographic optimization.…”

Section: Related Workmentioning

confidence: 99%

Boolean lexicographic optimization: algorithms & applications

Marques-Silva

Argelich

Graça

et al. 2011

Ann Math Artif Intell

View full text Add to dashboard Cite

Multi-Objective Combinatorial Optimization (MOCO) problems find a wide range of practical application problems, some of which involving Boolean variables and constraints. This paper develops and evaluates algorithms for solving MOCO problems, defined on Boolean domains, and where the optimality criterion is lexicographic. The proposed algorithms build on existing algorithms for either Maximum Satisfiability (MaxSAT), Pseudo-Boolean Optimization (PBO), or Integer Linear Programming (ILP). Experimental results, obtained on problem instances from haplotyping with pedigrees and software package dependencies, show that the proposed algorithms can provide significant performance gains over state of the art MaxSAT, PBO and ILP algorithms. Finally, the paper also shows that lexicographic optimization conditions are observed in the majority of the problem instances from the MaxSAT evaluations, motivating the development of dedicated algorithms that can exploit lexicographic optimization conditions in general MaxSAT problem instances.

show abstract

Section: Related Workmentioning

confidence: 99%

Boolean lexicographic optimization: algorithms & applications

Marques-Silva

Argelich

Graça

et al. 2011

Ann Math Artif Intell

View full text Add to dashboard Cite

show abstract

“…Such multi-objective optimisation problems aim to find a set of Pareto optimal solutions. A solution is called "Pareto optimal" if there is no other solution better than or equal to it in one of the objectives (Lukasiewycz et al, 2007). Hence, Pareto optimal solutions define a partial ordering, as opposed to the total ordering.…”

Section: Introductionmentioning

confidence: 99%

Self-tuning of software systems through dynamic quality tradeoff and value-based feedback control loop

Peng

Chen

et al. 2012

Journal of Systems and Software

View full text Add to dashboard Cite

Abstract. Quality requirements of a software system cannot be optimally met, especially when it is running in an uncertain and changing environment. In principle, a controller at runtime can monitor the change impact on quality requirements of the system, update the expectations and priorities from the environment, and take reasonable actions to improve the overall satisfaction. In practice, however, existing controllers are mostly designed for tuning low-level performance indicators instead of high-level requirements. By maintaining a live goal model to represent runtime requirements and linking the overall satisfaction of quality requirements to an indicator of earned business value, we propose a control-theoretic self-tuning method that can dynamically tune the preferences of different quality requirements, and can autonomously make tradeoff decisions through our preference-based goal reasoning procedure. The reasoning procedure results in an optimal configuration of the variation points by selecting the right alternative of ORdecomposed goals and such a configuration is mapped onto corresponding system architecture reconfigurations. The effectiveness of our self-tuning method is evaluated by earned business value, comparing our results with those obtained using static and ad-hoc methods.

show abstract

“…The proposed solution modifies a SAT backtracking algorithm to search first for optimal plans by branching according to the partial order induced by the preferences. In addition, algorithms for dealing with multi-objective PB problems have been developed [19], in contrast to traditional algorithms that optimize a single linear function.…”

Section: Introductionmentioning

confidence: 99%

On Solving Boolean Multilevel Optimization Problems

Argelich,

Lynce,

Marques-Silva

2009

Preprint

View full text Add to dashboard Cite

Many combinatorial optimization problems entail a number of hierarchically dependent optimization problems. An often used solution is to associate a suitably large cost with each individual optimization problem, such that the solution of the resulting aggregated optimization problem solves the original set of hierarchically dependent optimization problems. This paper starts by studying the package upgradeability problem in software distributions. Straightforward solutions based on Maximum Satisfiability (MaxSAT) and pseudo-Boolean (PB) optimization are shown to be ineffective, and unlikely to scale for large problem instances. Afterwards, the package upgradeability problem is related to multilevel optimization. The paper then develops new algorithms for Boolean Multilevel Optimization (BMO) and highlights a large number of potential applications. The experimental results indicate that the proposed algorithms for BMO allow solving optimization problems that existing MaxSAT and PB solvers would otherwise be unable to solve.

show abstract

Solving Multi-objective Pseudo-Boolean Problems

Cited by 6 publications

References 19 publications

Boolean lexicographic optimization: algorithms & applications

Boolean lexicographic optimization: algorithms & applications

Self-tuning of software systems through dynamic quality tradeoff and value-based feedback control loop

On Solving Boolean Multilevel Optimization Problems

Contact Info

Product

Resources

About