Multiobjective real-coded bayesian optimization algorithmrevisited: diversity preservation

Authors:
Chang Wook Ahn;R. S. Ramakrishna
Affiliations:
Gwangju Institute of Science and Technology, Gwangju, South Korea;Gwangju Institute of Science and Technology, Gwangju, South Korea
Venue:
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Year:
2007

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Abstract

This paper provides empirical studies on MrBOA, which have been designed for strengthening diversity of nondominated solutions. The studies lead to modified sharing. A new selection scheme has been suggested for improving diversity performance. Empirical tests validate their effectiveness on uniformity and front-spread (i.e., diversity) of nondominated set. A diversity-preserving MrBOA (dp-MrBOA) has been designed by carefully combining all the promising components; i.e., modified sharing, dynamic crowding, and diversity-preserving selection. Experiments demonstrate that the dp-MrBOA is able to significantly improve diversity performance (for the scaling problems), without weakening proximity of nondominated set.