Simulation-Based Methodologies for Global Optimization and Planning
Final rept. 1 Jul 2010-30 Jun 2013
MARYLAND UNIV COLLEGE PARK OFFICE OF RESEARCH ADMINISTRATION AND ADVANCEMENT
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The researchers made significant progress in all of the proposed research areas. The first major task in the proposal involved model-based randomized methods for global optimization. In support of this task, the researchers developed new methods for stochastic derivative estimators for discontinuous payoff functions the method includes Infinitesimal Perturbation Analysis and the Likelihood Ratio method as special cases and can be applied to functions of more general forms containing indicator functions. The researchers developed a new method of distributed ordinal comparison of selecting the best option, which maximizes the average of local reward function values among available options in a dynamic network. They discovered a new innovative approach to simulation-based global optimization by building a connection between global optimization and evolutionary games, as well as another new approach that exploits particle filtering they have summarized our model-based results in a comprehensive survey paper. The researchers also made significant progress in other model-based randomized methods, including a stochastic search algorithm for solving general optimization problems with little structure the algorithm iteratively finds high quality solutions by randomly sampling candidate solutions from a parameterized distribution model over the solution space.
- Operations Research