TY - GEN
T1 - Sampling in latent space for a mulitiobjective estimation of distribution algorithm
AU - Dong, Bing
AU - Zhou, Aimin
AU - Zhang, Guixu
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/14
Y1 - 2016/11/14
N2 - A regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA) has been proposed for continuous multiobjective optimization problems. Generating promising solutions to approximate the population is significant to RM-MEDA. In the reproduction of RM-MEDA, it adopts a Latin square design strategy to sample points in the latent space that is extended to cover the whole Pareto set. However, the setting of the extension scale is problem-dependent to some extent. To circumvent this issue, we propose a differential evolution based sampling (DES) scheme for RM-MEDA. DES mutates the projections of the parent solutions in the latent space to generate promising candidate offspring solutions. The empirical experiment results have shown the significant advantages of the DES scheme comparing to the Latin square design.
AB - A regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA) has been proposed for continuous multiobjective optimization problems. Generating promising solutions to approximate the population is significant to RM-MEDA. In the reproduction of RM-MEDA, it adopts a Latin square design strategy to sample points in the latent space that is extended to cover the whole Pareto set. However, the setting of the extension scale is problem-dependent to some extent. To circumvent this issue, we propose a differential evolution based sampling (DES) scheme for RM-MEDA. DES mutates the projections of the parent solutions in the latent space to generate promising candidate offspring solutions. The empirical experiment results have shown the significant advantages of the DES scheme comparing to the Latin square design.
KW - Differential evolution
KW - Estimation of distribution algorithm
KW - Latent space
KW - Multiobjective optimization
UR - https://www.scopus.com/pages/publications/85008251846
U2 - 10.1109/CEC.2016.7744172
DO - 10.1109/CEC.2016.7744172
M3 - 会议稿件
AN - SCOPUS:85008251846
T3 - 2016 IEEE Congress on Evolutionary Computation, CEC 2016
SP - 3027
EP - 3034
BT - 2016 IEEE Congress on Evolutionary Computation, CEC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE Congress on Evolutionary Computation, CEC 2016
Y2 - 24 July 2016 through 29 July 2016
ER -