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The parallel multiobjective EGO algorithm

  1. The multiobjective EGO algorithm using EIM (expected improvement matrix) criteria, which is significant cheaper to evaluate than the state-of-the-art multiobjective EI criteria. For detailed description about the EIM criteria, please refer to [1].
  2. The parallel multiobjective EGO algorithm using PEIM (Pseudo Expected Improvement Matrix) criteria, which is able to select multiple candidates in each cycle to evaluate in parallel [2].
  3. The dace toolbox [2] is used for building the Kriging models in the implementations.
  4. The non-dominated sorting method by Yi Cao [3] is used to identify the non-dominated fronts from all the design points
  5. The hypervolume indicators are calculated using the faster algorithm of [4] Nicola Beume et al. (2009).
  6. You need MATLAB 2016b or newer version and the global optimization toolbox to run the codes.

Reference

  1. D. Zhan, Y. Cheng, J. Liu, Expected Improvement Matrix-based Infill Criteria for Expensive Multiobjective Optimization, IEEE Transactions on Evolutionary Computation, 2017, 21 (6): 956-975.
  2. D. Zhan ,J. Qian ,J. Liu , et al. Pseudo Expected Improvement Matrix Criteria for Parallel Expensive Multi-objective Optimization. In Advances in Structural and Multidisciplinary Optimization: Proceedings of the 12th World Congress of Structural and Multidisciplinary Optimization (WCSMO12), Schumacher, A.,Vietor, T.,Fiebig, S., et al., Eds. Springer International Publishing: Cham, 2018; 175-190.
  3. Lophaven SN, Nielsen HB, and Sodergaard J, DACE - A MATLAB Kriging Toolbox, Technical Report IMM-TR-2002-12, Informatics and Mathematical Modelling, Technical University of Denmark, 2002. Available at: http://www2.imm.dtu.dk/projects/dace/.
  4. http://www.mathworks.com/matlabcentral/fileexchange/17251-pareto-front.
  5. N. Beume, C.M. Fonseca, M. Lopez-Ibanez, L. Paquete, J. Vahrenhold, On the Complexity of Computing the Hypervolume Indicator, IEEE Transactions on Evolutionary Computation 13(5) (2009) 1075-1082.

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