Constraint handling with modified hypervolume indicator for multi-objective optimization problems

Authors:
Zack Z. Zhu
Affiliations:
ETH Zurich, Zurich, Switzerland
Venue:
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Year:
2010

Citing 6
Cited 1

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Abstract

Many problems across various domains of research may be formulated as a multi-objective optimization problem. The Multi-objective Evolutionary Algorithm framework (MOEA) has been applied successfully to unconstrained multi-objective optimization problems. This work adapts the modified Hypervolume Indicator to incorporate constraints when used within the MOEA framework. Empirical results from a sample problem showed that the algorithm is capable of generating a high percentage of feasible solutions, while the shape parameter used to govern the desirability function make a trade-off between feasibility and Hypervolume. Furthermore, the shape parameter is shown to heavily influence the feasible solution's final goodness.