Tyler Chang, Layne T. Watson, Thomas Lux
Multiobjective optimization problems (MOPs) are common across many science and engineering fields. A multiobjective optimization algorithm (MOA) seeks to provide an approximation to the tradeoff surface between multiple, possibly conflicting, objectives. Many MOPs are the result of objective functions that require the evaluation of a computationally expensive numerical simulation. Solving these large and complex problems requires efficient coordination between the MOA and the computationally expensive cost functions. In this work, a recently proposed MOA is integrated into the libEnsemble software library, which coordinates extreme scale resources for large ensemble computations. Efficient integration requires fundamental changes to the underlying MOA. The convergence and performance results for the integrated and original MOA are compared on a set of benchmark problems.
Tyler H. Chang, Jeffrey Larson , Layne T. Watson, Thomas C. H. Lux: Managing Computationally Expensive Blackbox Multiobjective Optimization Problems Wwth Libensemble. SpringSim 2020: 1-12
- Date of publication:
- May 19, 2020
- Spring Simulation Multiconference
- Page number(s):