Abstract
Most existing multiobjective evolutionary algorithms (MOEAs) implicitly assume
that each objective function can be evaluated within the same period of time.
Typically. this is untenable in many real-world optimization scenarios where
evaluation of different objectives involves different computer simulations or
physical experiments with distinct time complexity. To address this issue, a
transfer learning scheme based on surrogate-assisted evolutionary algorithms
(SAEAs) is proposed, in which a co-surrogate is adopted to model the functional
relationship between the fast and slow objective functions and a transferable
instance selection method is introduced to acquire useful knowledge from the
search process of the fast objective. Our experimental results on DTLZ and UF
test suites demonstrate that the proposed algorithm is competitive for solving
bi-objective optimization where objectives have non-uniform evaluation
times.