Journal
EVOLUTIONARY COMPUTATION
Volume 30, Issue 4, Pages 531-+Publisher
MIT PRESS
DOI: 10.1162/evco_a_00311
Keywords
Black-box optimization; constraint handling; evolution strategies; active-set techniques
Funding
- Austrian Science Fund FWF [P29651N32]
- theNatural Sciences and Engineering Research Council of Canada (NSERC) [RGPIN-2020-04833]
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This paper proposes an active-set evolution strategy for evolutionary black-box optimization with explicit constraints, and experimentally demonstrates its advantage over other algorithms on the problem set.
Active-set approaches are commonly used in algorithms for constrained numerical optimization. We propose that active-set techniques can beneficially be employed for evolutionary black-box optimization with explicit constraints and present an active-set evolution strategy. We experimentally evaluate its performance relative to those of several algorithms for constrained optimization and find that the active-set evolution strategy compares favourably for the problem set under consideration.
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