Expected hypervolume improvement algorithm for PID controller tuning and the multiobjective dynamical control of a biogas plant

Kaifeng Yang, Daniel Gaida, Thomas Back, Michael Emmerich

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

15 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1934-1942
Number of pages9
ISBN (Electronic)9781479974924
DOIs
Publication statusPublished - 10 Sept 2015
EventIEEE Congress on Evolutionary Computation, CEC 2015 - Sendai, Japan
Duration: 25 May 201528 May 2015

Publication series

Name2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

Conference

ConferenceIEEE Congress on Evolutionary Computation, CEC 2015
Country/TerritoryJapan
CitySendai
Period25.05.201528.05.2015

Keywords

  • Biogas Plant
  • Efficient Global Optimization
  • Expected Hypervolume Improvement
  • PID Parameter Tuning
  • Surrogate-Assisted Multiobjective Optimization

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