TY - GEN
T1 - Expected hypervolume improvement algorithm for PID controller tuning and the multiobjective dynamical control of a biogas plant
AU - Yang, Kaifeng
AU - Gaida, Daniel
AU - Back, Thomas
AU - Emmerich, Michael
N1 - Funding Information:
Kaifeng Yang acknowledges financial support from China Scholarship Council (CSC), CSC No.201306370037. Michael Emmerich and Thomas Bäck acknowledge financial support by the PROMIMOOC-Process Mining for Multi-objective Online Control-project, supported by the Data Science program 'Challenging Big Data' of the Dutch Organisation for Scientific Research (NWO).
Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/10
Y1 - 2015/9/10
N2 - This paper presents and analyses an engineered expected hypervolume improvement (EHVI) algorithm for solving the problem of PID parameter tuning and the optimization problem of controlling the substrate feed of a biogas plant. The EHVI is the expected value of the increment of the hypervolume indicator given a Pareto front approximation and a predictive multivariate Gaussian distribution of a new point. To solve this problem, S-metric selection-based efficient global optimization (SMS-EGO), EHVI based efficient global optimization (EHVIEGO) and SMS-EMOA are used and compared in both the PID parameter tuning problem and for biogas plant feed optimization. The results of the experiments show that surrogate model based algorithms perform better than SMS-EMOA, and the performance of EHVI-EGO is slightly better than SMS-EGO.
AB - This paper presents and analyses an engineered expected hypervolume improvement (EHVI) algorithm for solving the problem of PID parameter tuning and the optimization problem of controlling the substrate feed of a biogas plant. The EHVI is the expected value of the increment of the hypervolume indicator given a Pareto front approximation and a predictive multivariate Gaussian distribution of a new point. To solve this problem, S-metric selection-based efficient global optimization (SMS-EGO), EHVI based efficient global optimization (EHVIEGO) and SMS-EMOA are used and compared in both the PID parameter tuning problem and for biogas plant feed optimization. The results of the experiments show that surrogate model based algorithms perform better than SMS-EMOA, and the performance of EHVI-EGO is slightly better than SMS-EGO.
KW - Biogas Plant
KW - Efficient Global Optimization
KW - Expected Hypervolume Improvement
KW - PID Parameter Tuning
KW - Surrogate-Assisted Multiobjective Optimization
UR - http://www.scopus.com/inward/record.url?scp=84963574884&partnerID=8YFLogxK
U2 - 10.1109/CEC.2015.7257122
DO - 10.1109/CEC.2015.7257122
M3 - Conference contribution
AN - SCOPUS:84963574884
T3 - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
SP - 1934
EP - 1942
BT - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Congress on Evolutionary Computation, CEC 2015
Y2 - 25 May 2015 through 28 May 2015
ER -