access_time 29 de janeiro de 2020 às 15:00 até 29 de janeiro de 2020 às 16:00
place Room E2 (Piso -1), Torre Norte, IST, Alameda
Complete methods for solving Multi-Objective Combinatorial Optimization (MOCO) problems are usually based in the classic branch and bound techniques. More recently, new constraint-based algorithms have been proposed based on the enumeration of $P$-minimal models or the enumeration of Minimal Correction Subsets (MCSs). In single-objective optimization problems such as Maximum Satisfiability (MaxSAT), several state of the art algorithms are based on over-constrained formulas that are iteratively relaxed until an optimal solution is found. In this work, we propose the first algorithm for MOCO based on over-constrained formulas. Moreover, our algorithm naturally takes advantage of incrementality commonly used in unsatisfiability-based MaxSAT algorithms.
local_offer Tópicos de Investigação
person Candidato: João O'Neill Cortes
supervisor_account Orientador: Prof. Vasco Manquinho / Prof.ª Inês Lynce