Abstract
This article provides an algorithm that is dedicated to repeated trajectory optimization with a fixed horizon and addresses processes that are difficult to describe by the established laws of physics. Typically, soft-computing methods are used in such cases, i.e. black-box modeling and evolutionary optimization. Both suffer from high dimensions that make the problems complex or even computationally infeasible. We propose a way how to start from very simple problems and - after the simple problems are covered sufficiently - proceed to more complex ones. We provide also a case study related to the dynamic optimization of the HVAC (heating, ventilation, and air conditioning) systems.
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Macek, K., Rojíček, J., Bičík, V. (2013). Trajectory Optimization under Changing Conditions through Evolutionary Approach and Black-Box Models with Refining. In: Omatu, S., Neves, J., Rodriguez, J., Paz Santana, J., Gonzalez, S. (eds) Distributed Computing and Artificial Intelligence. Advances in Intelligent Systems and Computing, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-319-00551-5_33
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DOI: https://doi.org/10.1007/978-3-319-00551-5_33
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-00550-8
Online ISBN: 978-3-319-00551-5
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