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Surrogate-Assisted Evolutionary Framework for Data-Driven Dynamic Optimization
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DOI：10.1109/TETCI.2018.2872029.png)
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Recently, dynamic optimization has received much attention from the swarm and evolutionary computation community. However, few studies have investigated data-driven evolutionary dynamic optimization, and most algorithms for evolutionary dynamic optimization are based on analytical mathematical functions. In this paper, we investigate data-driven evolutionary dynamic optimization. First, we develop a surrogate-assisted evolutionary framework for solving data-driven dynamic optimization problems (DD-DOPs). Second, we employ a benchmark based on the typical dynamic optimization problems set in order to verify the performance of the proposed framework. The experimental results demonstrate that the proposed framework is effective for solving DD-DOPs.
Keyword:
Data-driven optimization
dynamic optimization
surrogate-assisted evolutionary algorithm
surrogate model
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