Diagnostic assessment of the borg MOEA for many-objective product family design problems

David Hadka, Patrick M. Reed, Timothy William Simpson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

44 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Title of host publication2012 IEEE Congress on Evolutionary Computation, CEC 2012
DOIs
StatePublished - Oct 4 2012
Event2012 IEEE Congress on Evolutionary Computation, CEC 2012 - Brisbane, QLD, Australia
Duration: Jun 10 2012Jun 15 2012

Publication series

Name2012 IEEE Congress on Evolutionary Computation, CEC 2012

Other

Other2012 IEEE Congress on Evolutionary Computation, CEC 2012
CountryAustralia
CityBrisbane, QLD
Period6/10/126/15/12

Fingerprint

Product Family
Multi-objective Evolutionary Algorithm
Evolutionary algorithms
Diagnostics
Aviation
Sensitivity analysis
Recombination
Aircraft
Sensitivity Analysis
Adaptive Behavior
Parameter Sensitivity
Global Analysis
Design
Mutation
Benchmark
Operator
Range of data

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Theoretical Computer Science

Cite this

Hadka, D., Reed, P. M., & Simpson, T. W. (2012). Diagnostic assessment of the borg MOEA for many-objective product family design problems. In 2012 IEEE Congress on Evolutionary Computation, CEC 2012 [6256466] (2012 IEEE Congress on Evolutionary Computation, CEC 2012). https://doi.org/10.1109/CEC.2012.6256466
Hadka, David ; Reed, Patrick M. ; Simpson, Timothy William. / Diagnostic assessment of the borg MOEA for many-objective product family design problems. 2012 IEEE Congress on Evolutionary Computation, CEC 2012. 2012. (2012 IEEE Congress on Evolutionary Computation, CEC 2012).
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Hadka, D, Reed, PM & Simpson, TW 2012, Diagnostic assessment of the borg MOEA for many-objective product family design problems. in 2012 IEEE Congress on Evolutionary Computation, CEC 2012., 6256466, 2012 IEEE Congress on Evolutionary Computation, CEC 2012, 2012 IEEE Congress on Evolutionary Computation, CEC 2012, Brisbane, QLD, Australia, 6/10/12. https://doi.org/10.1109/CEC.2012.6256466

Diagnostic assessment of the borg MOEA for many-objective product family design problems. / Hadka, David; Reed, Patrick M.; Simpson, Timothy William.

2012 IEEE Congress on Evolutionary Computation, CEC 2012. 2012. 6256466 (2012 IEEE Congress on Evolutionary Computation, CEC 2012).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Hadka D, Reed PM, Simpson TW. Diagnostic assessment of the borg MOEA for many-objective product family design problems. In 2012 IEEE Congress on Evolutionary Computation, CEC 2012. 2012. 6256466. (2012 IEEE Congress on Evolutionary Computation, CEC 2012). https://doi.org/10.1109/CEC.2012.6256466