On Constraint Handling in Surrogate-Assisted Evolutionary Many-Objective Optimization
Chugh, T., Sindhya, K., Miettinen, K., Hakanen, J., & Jin, Y. (2016). On Constraint Handling in Surrogate-Assisted Evolutionary Many-Objective Optimization. In J. Handl, E. Hart, P. R. Lewis, M. López-Ibáñez, G. Ochoa, & B. Paechter (Eds.), Parallel Problem Solving from Nature – PPSN XIV : 14th International Conference, Edinburgh, UK, September 17-21, 2016, Proceedings (pp. 214-224). Lecture Notes in Computer Science, 9921. Springer International Publishing. doi:10.1007/978-3-319-45823-6_20
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Lecture Notes in Computer Science;9921Date
2016Discipline
TietotekniikkaCopyright
© Springer International Publishing AG. This is a final draft version of an article whose final and definitive form has been published by Springer. Published in this repository with the kind permission of the publisher.
Surrogate-assisted evolutionary multiobjective optimization
algorithms are often used to solve computationally expensive problems.
But their efficacy on handling constrained optimization problems having
more than three objectives has not been widely studied. Particularly
the issue of how feasible and infeasible solutions are handled in generating
a data set for training a surrogate has not received much attention.
In this paper, we use a recently proposed Kriging-assisted evolutionary
algorithm for many-objective optimization and investigate the effect of
infeasible solutions on the performance of the surrogates. We assume
that constraint functions are computationally inexpensive and consider
different ways of handling feasible and infeasible solutions for training the
surrogate and examine them on different benchmark problems. Results
on the comparison with a reference vector guided evolutionary algorithm
show that it is vital for the success of the surrogate to properly
...
