Journal
EVOLUTIONARY COMPUTATION
Volume 20, Issue 3, Pages 423-452Publisher
MIT PRESS
DOI: 10.1162/EVCO_a_00053
Keywords
Evolutionary computation; multiobjective optimization; many-objective optimization; search control; parameterization
Funding
- National Science Foundation [OCI-0821527]
- Directorate For Geosciences [1240507] Funding Source: National Science Foundation
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The growing popularity of multiobjective evolutionary algorithms (MOEAs) for solving many-objective problems warrants the careful investigation of their search controls and failure modes. This study contributes a new diagnostic assessment framework for rigorously evaluating the effectiveness, reliability, efficiency, and controllability of MOEAs as well as identifying their search controls and failure modes. The framework is demonstrated using the recently introduced Borg MOEA, epsilon-NSGA-II, epsilon-MOEA, IBEA, OMOPSO, GDE3, MOEA/D, SPEA2, and NSGA-II on 33 instances of 18 test problems from the DTLZ, WFG, and CEC 2009 test suites. The diagnostic framework exploits Sobol's variance decomposition to provide guidance on the algorithms' non-separable, multi-parameter controls when performing a many-objective search. This study represents one of the most comprehensive empirical assessments of MOEAs ever completed.
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