MATSuMoTo: The MATLAB Surrogate Model Toolbox For Computationally Expensive Black-Box Global Optimization Problems

MATSuMoTo is the MATLAB Surrogate Model Toolbox for computationally expensive, black-box, global optimization problems that may have continuous, mixed-integer, or pure integer variables. Due to the black-box nature of the objective function, derivatives are not available. Hence, surrogate models are used as computationally cheap approximations of the expensive objective function in order to guide the search for improved solutions. Due to the computational expense of doing a single function evaluation, the goal is to find optimal solutions within very few expensive evaluations. The multimodality of the expensive black-box function requires an algorithm that is able to search locally as well as globally. MATSuMoTo is able to address these challenges. MATSuMoTo offers various choices for surrogate models and surrogate model mixtures, initial experimental design strategies, and sampling strategies. MATSuMoTo is able to do several function evaluations in parallel by exploiting MATLAB's Parallel Computing Toolbox.Comment: 13 pages, 7 figure

The Calibration of Traffic Simulation Models : Report on the assessment of different Goodness of Fit measures and Optimization Algorithms MULTITUDE Project – COST Action TU0903

In the last decades, simulation optimization has received considerable attention from both researchers and practitioners. Simulation optimization is the process of finding the best values of some decision variables for a system whose performance is evaluated using the output of a simulation model. A possible example of simulation optimization is the model calibration. In traffic modelling this topic is particularly relevant since the solutions to the methodological issues arising when setting up a calibration study cannot be posed independently. This calls for methodologies able to check the robustness of a calibration framework as well as further investigations of the issue, in order to identify possible “classes” of problems to be treated in a similar way. Therefore in the present work, first a general method for verifying a traffic micro-simulation calibration procedure (suitable in general for simulation optimization) is described, based on a test with synthetic data. Then it is applied, my means of two different case studies, to draw inferences on the effect that different combinations of parameters to calibrate, optimization algorithm, measures of Goodness of Fit and noise in the data may have on the optimization problem. Results showed the importance of verifying the calibration procedure with synthetic data. In addition they ascertained the need for global optimization solutions, giving new insights into the topic. Research contained within this paper benefited from the participation in EU COST Action TU0903 MULTITUDEJRC.H.8-Sustainability Assessmen

A Stochastic Dynamic Programming Approach To Balancing Wind Intermittency With Hydropower

Hydropower is a rapid response energy source and thus a perfect complement to the intermittency of wind power. However, the effect wind energy has on conventional hydropower systems can be felt, especially if the system is subject to several other environmental and non-power use constraints. The goal of this paper is to develop a general method for optimizing short-term hydropower operations of a realistic multireservoir hydropower system in a deregulated market setting when there is a stochastic wind input. The approach used is a modification of stochastic dynamic programming (SDP). The methodology is applied to a representation of multiple projects in the Federal Columbia River Power System, which is currently being dispatched by the Bonneville Power Administration. Currently, studies on hydropower operations optimization with wind have involved linear programming orstochastic programming, which are based on linearity of the objective function and constraints. SDP, by contrast, is a stochastic optimization method that does not require assumptions of linearity of the objective function or the constraints. The true adaptive and stochastic nonlinear formulation of the objective function can be applied to multiple timesteps, and is efficient for many timesteps compared to stochastic programming