TY - GEN
T1 - Multi-objective evolutionary algorithms' performance in a support role
AU - Woodruff, Matthew J.
AU - Simpson, Timothy W.
AU - Reed, Patrick M.
N1 - Funding Information:
This research is part of the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation (award number OCI 07-25070) and the state of Illinois. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications. Other computational resources for this work were provided in part through instrumentation funded by the National Science Foundation through Grant OCI-0821527. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the US National Science Foundation.
Publisher Copyright:
Copyright © 2015 by ASME.
PY - 2015
Y1 - 2015
N2 - This paper presents a diagnostic assessment study, evaluating five leading multi-objective evolutionary algorithms (MOEAs) on their effectiveness, efficiency, reliability, and controllability on four different formulations of the same benchmark conceptual design problem, using the same underlying model. This assessment entails a broad sampling of the parameter space of each MOEA, for each problem formulation, requiring millions of optimization runs and trillions of model evaluations. The results of this assessment show the strengths and limitations of these MOEAs, establishing the Borg MOEA as a leading algorithm.
AB - This paper presents a diagnostic assessment study, evaluating five leading multi-objective evolutionary algorithms (MOEAs) on their effectiveness, efficiency, reliability, and controllability on four different formulations of the same benchmark conceptual design problem, using the same underlying model. This assessment entails a broad sampling of the parameter space of each MOEA, for each problem formulation, requiring millions of optimization runs and trillions of model evaluations. The results of this assessment show the strengths and limitations of these MOEAs, establishing the Borg MOEA as a leading algorithm.
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U2 - 10.1115/DETC201546891
DO - 10.1115/DETC201546891
M3 - Conference contribution
AN - SCOPUS:84978976569
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 41st Design Automation Conference
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2015
Y2 - 2 August 2015 through 5 August 2015
ER -