This paper proposes a new heuristic approach for solving optimal discrete-valued control problems. We illustrate the approach with an existing hybrid power system model. The problem of choosing an operating schedule to minimize generator, battery, and switching costs is first posed as a mixed discrete dynamic optimization problem. Then, a discrete filled function method is employed in conjunction with a computational optimal control technique to solve this problem. Computational results indicate that this approach is robust, efficient, and can successfully identify a near-global solution for this complex applied optimization problem despite the presence of multiple local optima.
Many real life problems can be modeled as nonlinear discrete optimization problems. Such problems often have multiple local minima and thus require global optimization methods.Due to high complexity of these problems, heuristic based global optimization techniques are usually required when solving large scale discrete optimization or mixed discrete optimization problems. One of the more recent global optimization tools is known as the discrete filled function method. Nine variations of the discrete filled function method in literature are identified and a review on theoretical properties of each method is given.Some of the most promising filled functions are tested on various benchmark problems.Numerical results are given for comparison.
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