Fast stochastic global optimization methods and their applications to cluster crystallization and protein docking

copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii Two global optimization methods are proposed in this thesis. They are the multi-canonical basin hopping (MUBH) method and the basin paving (BP) method. The MUBH method combines the basin hopping (BH) method, which can be used to efficiently map out an energy landscape associated with local minima, with the multicanonical Monte Carlo (MUCA) method, which encourages the system to move out of energy traps during the computation. It is found to be more efficient than the original BH method when applied to the Lennard-Jones systems containing 150–185 particles. The asynchronous multicanonical basin hopping (AMUBH) method, a paral-lelization of the MUBH method, is also implemented using the message passin