Simulated Annealing and its Applications

Simulated annealing has proven to be a good technique for solving hard combina-torial optimization problems. Some attempts at speeding up annealing algorithms have been based on shared memory multiprocessor systems. Also parallelizations for certain problems on distributed memory multiprocessor systems are known. In this paper, we present a problem independent general purpose parallel implementation of simulated annealing on large distributed memory message-passing mul-tiprocessor systems. The sequential algorithm is studied and we give a classiica-tion of combinatorial optimization problems together with their neighborhood structures. Several parallelization approaches are examined considering their suitability for problems of the various classes. For typical representatives of the diierent classes good parallel simulated annealing implementations are presented. We describe in detail several 'tricks' increasing eeciency and attained solution quality of the diierent parallel implementations. Extensive measurements of eeciency, solution quality and other parameters of the algorithms are presented on diierent numbers of processors. These measurements show, that our algorithms scale up to more that 120 processors. Some applications are described in detail, showing the practical relevance of our work. All algorithms are implemented in OCCAM-2 on a free conngurable transputer system.