TY - GEN
T1 - Diagnostic assessment of the borg MOEA for many-objective product family design problems
AU - Hadka, David
AU - Reed, Patrick M.
AU - Simpson, Timothy W.
PY - 2012
Y1 - 2012
N2 - The recently introduced Borg multiobjective evolutionary algorithm (MOEA) framework features auto-adaptive search that tailors itself to effectively explore different problem spaces. A key auto-adaptive feature of the Borg MOEA is the dynamic allocation of search across a suite of recombination and mutation operators. This study explores the application of the Borg MOEA on a real-world product family design problem: the severely constrained, ten objective General Aviation Aircraft (GAA) problem. The GAA problem represents a promising benchmark problem that strongly highlights the importance of using auto-adaptive search to discover how to exploit multiple recombination strategies cooperatively. The auto-adaptive behavior of the Borg MOEA is rigorously compared against its ancestor algorithm, the ε-MOEA, by employing global sensitivity analysis across each algorithm's feasible parameter ranges. This study provides the first Sobol' sensitivity analysis to determine the individual and interactive parameter sensitivities of MOEAs on a real-world many-objective problem.
AB - The recently introduced Borg multiobjective evolutionary algorithm (MOEA) framework features auto-adaptive search that tailors itself to effectively explore different problem spaces. A key auto-adaptive feature of the Borg MOEA is the dynamic allocation of search across a suite of recombination and mutation operators. This study explores the application of the Borg MOEA on a real-world product family design problem: the severely constrained, ten objective General Aviation Aircraft (GAA) problem. The GAA problem represents a promising benchmark problem that strongly highlights the importance of using auto-adaptive search to discover how to exploit multiple recombination strategies cooperatively. The auto-adaptive behavior of the Borg MOEA is rigorously compared against its ancestor algorithm, the ε-MOEA, by employing global sensitivity analysis across each algorithm's feasible parameter ranges. This study provides the first Sobol' sensitivity analysis to determine the individual and interactive parameter sensitivities of MOEAs on a real-world many-objective problem.
UR - http://www.scopus.com/inward/record.url?scp=84866883070&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84866883070&partnerID=8YFLogxK
U2 - 10.1109/CEC.2012.6256466
DO - 10.1109/CEC.2012.6256466
M3 - Conference contribution
AN - SCOPUS:84866883070
SN - 9781467315098
T3 - 2012 IEEE Congress on Evolutionary Computation, CEC 2012
BT - 2012 IEEE Congress on Evolutionary Computation, CEC 2012
T2 - 2012 IEEE Congress on Evolutionary Computation, CEC 2012
Y2 - 10 June 2012 through 15 June 2012
ER -