Approximating the Knee of an MOP with Stochastic Search Algorithms

Authors:
Oliver Schütze;Marco Laumanns;Carlos A. Coello Coello
Affiliations:
CINVESTAV-IPN, Computer Science Department, Mexico City, Mexico;ETH Zurich, Institute for Operations Research, Zurich, Switzerland;CINVESTAV-IPN, Computer Science Department, Mexico City, Mexico
Venue:
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Year:
2008

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Abstract

In this paper we address the problem of approximating the 'knee' of a bi-objective optimization problem with stochastic search algorithms. Knees or entire knee-regions are of particular interest since such solutions are often preferred by the decision makers in many applications. Here we propose and investigate two update strategies which can be used in combination with stochastic multi-objective search algorithms (e.g., evolutionary algorithms) and aim for the computation of the knee and the knee-region, respectively. Finally, we demonstrate the applicability of the approach on two examples.