Intelligent Algorithms Based On Simulations And Their Applications

There exist a great deal of uncertainties such as randomness, fuzziness and fuzzyrandomness in the fields of management science, computer science, system science, information science, engineering, etc. Many decisions in these fields need to be made under the uncertainties. Uncertain programming is a powerful tool to handle the decision-making problems. This dissertation proposes many intelligent algorithms to solve the uncertain programming models, then studies the multiproduct aggregate production planning (APP) problems in fuzzy random environments. The contents are described as follows:In many cases, it is very difficult or impossible to obtain the exact values of the uncertain functions with fuzzy variables, fuzzy random variables, and random fuzzy variables. Therefore, it is very necessary to estimate these values by simulations. This dissertation proposes the simultaneous perturbation stochastic approximation (SPSA) algorithms based on simulations (fuzzy simulation, fuzzy random simulation, and random fuzzy simulation) to solve the fuzzy programming models, fuzzy random programming models, and random fuzzy programming models. The algorithms can converge fast to the local optimal solutions. In many real optimization problems, due to the limitations on the resources employed in the problems, a local optimal solution may be accepted.In the SPSA algorithms based on simulations, much time will be spent on the simulations. Therefore, this dissertation designs the SPSA algorithms combining simulations with neural network (NN). First, simulations are used to generate a set of input-output data for the uncertain functions. Then the data are employed to train an NN, which is embedded in the SPSA algorithms. The algorithms can converge faster to the local optimal solutions than the SPSA algorithms based on simulations.For the optimization problems where the global optimal solution is needed, this dissertation designs hybrid optimization algorithms based on simulations. The algorithms integrate simulations, NN, genetic algorithm (GA), and SPSA. First, simulations are used to generate a set of input-output data for the uncertain functions. Then the data are employed to train an NN, which is embedded in GA and the SPSA algorithm. GA is employed to search the optimal solution...