Applied Intelligence

, Volume 49, Issue 3, pp 1098–1126 | Cite as

Multiobjective optimal control for wastewater treatment process using adaptive MOEA/D

  • Hongbiao Zhou
  • Junfei QiaoEmail author


Through the analysis of the biological wastewater treatment process (WWTP), a multiobjective optimal control strategy is developed with the usage of energy consumption (EC) and effluent quality (EQ) as objectives to be optimized. To effectively handle the multiobjective optimization problem (MOP) with complex Pareto-optimal front (POF), an adaptive multiobjective evolutionary algorithm based on decomposition (AMOEA/D) is proposed in this paper. Since the efficiency of the multiple reference points and two-phase optimization strategies in solving MOPs with complex POFs has been proved. In the proposed AMOEA/D, an auto-switching strategy based on the aggregation function enhancement is designed to automatically make the algorithm switch from the first phase to the second phase. Besides, an adaptive differential evolution strategy is introduced into AMOEA/D to balance exploration and exploitation during the evolutionary process. Finally, the dynamic optimization, intelligent decision and bottom tracking control of the set-points of the dissolved oxygen and nitrate nitrogen in the WWTP are achieved via the combination of AMOEA/D with the self-organizing fuzzy neural network approximator and the self-organizing fuzzy neural network controller. The international benchmark simulation model No. 1 (BSM1) is utilized for experimental verification. Simulation results demonstrate that the proposed AMOEA/D can effectively reduce the EC of the WWTP under the premise of ensuring effluent parameters to meet the effluent discharge standards.


Wastewater treatment process Multiobjective optimal control MOEA/D Two-phase optimization Auto-switching Adaptive differential evolution strategy 



The authors would like to thank the Editor-in-Chief, the Associate Editor and anonymous reviewers for their invaluable suggestions which have been incorporated to improve the quality of the paper. This work was supported in part by the National Science Foundation for Distinguished Young Scholars of China under Grant 61225016 and the State Key Program of National Natural Science of China under Grant 61533002.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Information TechnologyBeijing University of TechnologyBeijingChina
  2. 2.Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijingChina
  3. 3.Faculty of AutomationHuaiyin Institute of TechnologyHuai’anChina

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