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Directed Multiobjective Optimization Based on the Weighted Hypervolume Indicator

Abstract : Recently, there has been a large interest in set-based evolutionary algorithms for multiobjective optimization. They are based on the definition of indicators that characterize the quality of the current population while being compliant with the concept of Pareto-optimality. It has been shown that the hypervolume indicator, which measures the dominated volume in the objective space, enables the design of efficient search algorithms and, at the same time, opens up opportunities to express user preferences in the search by means of weight functions. The present paper contains the necessary theoretical foundations and corresponding algorithms to (i) select appropriate weight functions, to (ii) transform user preferences into weight functions and to (iii) efficiently evaluate the weighted hypervolume indicator through Monte Carlo sampling. The algorithm W-HypE, which implements the previous concepts, is introduced, and the effectiveness of the search, directed towards the user's preferred solutions, is shown using an extensive set of experiments including the necessary statistical performance assessment.
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Contributor : Dimo Brockhoff Connect in order to contact the contributor
Submitted on : Tuesday, January 5, 2021 - 2:09:02 PM
Last modification on : Thursday, January 20, 2022 - 5:27:52 PM
Long-term archiving on: : Wednesday, April 7, 2021 - 9:31:22 AM


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Dimo Brockhoff, Johannes Bader, Lothar Thiele, Eckart Zitzler. Directed Multiobjective Optimization Based on the Weighted Hypervolume Indicator. Journal of Multi-Criteria Decision Analysis, 2013, Special Issue: Evolutionary Multiobjective Optimization: Methodologies and Applications, 20 (5-6), pp.291-317. ⟨10.1002/mcda.1502⟩. ⟨hal-00920178⟩



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