Evolutionary algorithm enhancement for model predictive control and real-time decision support

Andrea Zimmer, Arthur Schmidt, Avi Ostfeld, Barbara Minsker

Research output: Contribution to journalArticlepeer-review


Effective decision support and model predictive control of real-time environmental systems require that evolutionary algorithms operate more efficiently. A suite of model predictive control (MPC) genetic algorithms are developed and tested offline to explore their value for reducing combined sewer overflow (CSO) volumes during real-time use in a deep-tunnel sewer system. MPC approaches include the micro-GA, the probability-based compact GA, and domain-specific GA methods that reduce the number of decision variable values analyzed within the sewer hydraulic model, thus reducing algorithm search space. Minimum fitness and constraint values achieved by all GA approaches, as well as computational times required to reach the minimum values, are compared to large population sizes with long convergence times. Optimization results for a subset of the Chicago combined sewer system indicate that genetic algorithm variations with a coarse decision variable representation, eventually transitioning to the entire range of decision variable values, are best suited to address the CSO control problem. Although diversity-enhancing micro-GAs evaluate a larger search space and exhibit shorter convergence times, these representations do not reach minimum fitness and constraint values. The domain-specific GAs prove to be the most efficient for this case study. Further MPC algorithm developments are suggested to continue advancing computational performance of this important class of problems with dynamic strategies that evolve as the external constraint conditions change.

Original languageEnglish (US)
Pages (from-to)330-341
Number of pages12
JournalEnvironmental Modelling and Software
StatePublished - Jul 1 2015


  • Decision support
  • Genetic algorithms
  • Model predictive control
  • Real-time

ASJC Scopus subject areas

  • Software
  • Environmental Engineering
  • Ecological Modeling


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