Parallel Search-Based Methods in Optimization

Search-based methods like Branch and Bound and Branch and Cut are essential tools in solving diicult problems to optimality in the eld of combinatorial optimization, and much experience has been gathered regarding the design and implementation of parallel methods in this eld. Search-based methods appear, however, also in connection with certain continuous optimization problems and problems in Artiicial Intelligence, and parallel versions hereof have also been studied. Based on parallel Branch and Bound algorithms, the advantages as well as the diiculties and pitfalls in connection with parallel search-based methods are outlined. Experiences with parallel search-based methods from the three elds mentioned are then described and compared in order to reveal similarities as well as diierences across the elds.