A new hybrid mutation operator for multiobjective optimization with differential evolution |
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Authors: | Karthik Sindhya Sauli Ruuska Tomi Haanp?? Kaisa Miettinen |
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Affiliation: | (1) Department of Mathematical Information Technology, P.O. Box 35 (Agora), 40014 University of Jyv?skyl?, Finland |
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Abstract: | Differential evolution has become one of the most widely used evolutionary algorithms in multiobjective optimization. Its
linear mutation operator is a simple and powerful mechanism to generate trial vectors. However, the performance of the mutation
operator can be improved by including a nonlinear part. In this paper, we propose a new hybrid mutation operator consisting
of a polynomial-based operator with nonlinear curve tracking capabilities and the differential evolution’s original mutation
operator, for the efficient handling of various interdependencies between decision variables. The resulting hybrid operator
is straightforward to implement and can be used within most evolutionary algorithms. Particularly, it can be used as a replacement
in all algorithms utilizing the original mutation operator of differential evolution. We demonstrate how the new hybrid operator
can be used by incorporating it into MOEA/D, a winning evolutionary multiobjective algorithm in a recent competition. The
usefulness of the hybrid operator is demonstrated with extensive numerical experiments showing improvements in performance
compared with the previous state of the art. |
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