Targeting Well-Balanced Solutions in Multi-Objective Bayesian Optimization Under a Restricted Budget
Multi-objective optimization aims at finding trade-off solutions to conflicting objectives. These constitute the Pareto optimal set. In the context of expensive-to-evaluate functions, it is impossible and often non-informative to look for the entire set. As an end-user would typically prefer solutions with equilibrated trade-offs between the objectives, we define a Pareto front center. We then modify the Bayesian multi-objective optimization algorithm which uses Gaussian Processes to maximize the expected hypervolume improvement, to restrict the search to the Pareto front center. The cumulated effects of the Gaussian Processes and the center targeting strategy lead to a particularly efficient convergence to a critical part of the Pareto set.
KeywordsGaussian processes Parsimonious optimization Computer experiments Preference-based optimization
- 1.Bechikh, S., Kessentini, M., Said, L.B., Ghédira, K.: Chap. 4: Preference incorporation in evolutionary multiobjective optimization: a survey of the state-of-the-art. Adv. Comput. 98, 141–207 (2015)Google Scholar
- 3.Emmerich, M.T., Deutz, A.H., Klinkenberg, J.W.: Hypervolume-based expected improvement: monotonicity properties and exact computation. In: 2011 IEEE Congress on Evolutionary Computation (CEC), pp. 2147–2154. IEEE (2011)Google Scholar
- 5.Ponweiser, W., Wagner, T., Biermann, D., Vincze, M.: Multiobjective optimization on a limited budget of evaluations using model-assisted s-metric selection. In: International Conference on Parallel Problem Solving from Nature, pp. 784–794. Springer (2008)Google Scholar