GRASP—a new search algorithm for satisfiability
Proceedings of the 1996 IEEE/ACM international conference on Computer-aided design
Boolean satisfiability in electronic design automation
Proceedings of the 37th Annual Design Automation Conference
A machine program for theorem-proving
Communications of the ACM
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Efficient conflict driven learning in a boolean satisfiability solver
Proceedings of the 2001 IEEE/ACM international conference on Computer-aided design
Generic ILP versus specialized 0-1 ILP: an update
Proceedings of the 2002 IEEE/ACM international conference on Computer-aided design
A fast pseudo-boolean constraint solver
Proceedings of the 40th annual Design Automation Conference
An evolutionary approach to system-level synthesis
CODES '97 Proceedings of the 5th International Workshop on Hardware/Software Co-Design
The complexity of theorem-proving procedures
STOC '71 Proceedings of the third annual ACM symposium on Theory of computing
SAT-Based Techniques in System Synthesis
DATE '03 Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
Effective Lower Bounding Techniques for Pseudo-Boolean Optimization
Proceedings of the conference on Design, Automation and Test in Europe - Volume 2
Pueblo: A Modern Pseudo-Boolean SAT Solver
Proceedings of the conference on Design, Automation and Test in Europe - Volume 2
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Efficient symbolic multi-objective design space exploration
Proceedings of the 2008 Asia and South Pacific Design Automation Conference
On solving Boolean multilevel optimization problems
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Boolean lexicographic optimization: algorithms & applications
Annals of Mathematics and Artificial Intelligence
Journal of Systems and Software
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Integer Linear Programs are widely used in areas such as routing problems, scheduling analysis and optimization, logic synthesis, and partitioning problems. As many of these problems have a Boolean nature, i.e., the variables are restricted to 0 and 1, so called Pseudo-Boolean solvers have been proposed. They are mostly based on SAT solvers which took continuous improvements over the past years. However, Pseudo-Boolean solvers are only able to optimize a single linear function while fulfilling several constraints. Unfortunately many real-world optimization problems have multiple objective functions which are often conflicting and have to be optimized simultaneously, resulting in general in a set of optimal solutions. As a consequence, a single-objective Pseudo-Boolean solver will not be able to find this set of optimal solutions. As a remedy, we propose three different algorithms for solving multi-objective Pseudo-Boolean problems. Our experimental results will show the applicability of these algorithms on the basis of several test cases.