Improving many-objective optimizers with aggregate meta-models

In the field of multi-objective optimization there have been attempts to reduce the number of objective function evaluations by the use of surrogate models. However, in many-objective optimization, this work still has to be done to make the optimizers more practically usable. In this paper we show, that aggregate meta-models can be used even for the many-objective optimization and that they can also improve the performance of the many-objective optimizer. Moreover, meta-models are discussed from another point of view and compared to scalarization techniques in many-objective optimization. Two algorithms using our models are compared to IBEA on a set of selected benchmark functions with 5, 10, and 15 objectives.

[1]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[2]  Lothar Thiele,et al.  Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study , 1998, PPSN.

[3]  Roman Neruda,et al.  LAMM-MMA: multiobjective memetic algorithm with local aggregate meta-model , 2011, GECCO '11.

[4]  Eckart Zitzler,et al.  Indicator-Based Selection in Multiobjective Search , 2004, PPSN.

[5]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[6]  Eckart Zitzler,et al.  HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization , 2011, Evolutionary Computation.

[7]  Kyriakos C. Giannakoglou,et al.  Multiobjective Metamodel–Assisted Memetic Algorithms , 2009 .

[8]  Tapabrata Ray,et al.  An Evolutionary Algorithm with Spatially Distributed Surrogates for Multiobjective Optimization , 2007, ACAL.

[9]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[10]  Michèle Sebag,et al.  A mono surrogate for multiobjective optimization , 2010, GECCO '10.

[11]  Evan J. Hughes,et al.  Evolutionary many-objective optimisation: many once or one many? , 2005, 2005 IEEE Congress on Evolutionary Computation.

[12]  Michèle Sebag,et al.  Dominance-Based Pareto-Surrogate for Multi-Objective Optimization , 2010, SEAL.

[13]  Hisao Ishibuchi,et al.  Evolutionary many-objective optimization: A short review , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[14]  Michael T. M. Emmerich,et al.  Single- and multiobjective evolutionary optimization assisted by Gaussian random field metamodels , 2006, IEEE Transactions on Evolutionary Computation.

[15]  Roman Neruda,et al.  ASM-MOMA: Multiobjective memetic algorithm with aggregate surrogate model , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[16]  Thorsten Joachims,et al.  A support vector method for multivariate performance measures , 2005, ICML.

[17]  Andy J. Keane,et al.  Multi-Objective Optimization Using Surrogates , 2010 .