Surrogate Many Objective Optimization: Combining Evolutionary Search, \(\epsilon \)-Dominance and Connected Restarts

  • Taimoor AkhtarEmail author
  • Christine A. Shoemaker
  • Wenyu Wang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 991)


Scaling multi-objective optimization (MOO) algorithms to handle many objectives is a significant computational challenge. This challenge exacerbates when the underlying objectives are computationally expensive, and solutions are desired within a limited number of expensive objective evaluations. A surrogate model-based optimization framework can be effective in MOO. However, most prior model-based algorithms are effective for 2–3 objectives. This study investigates the combined use of \(\epsilon \)-dominance, connected restarts and evolutionary search for efficient Many-objective optimization (MaOO). We built upon an existing surrogate-based evolutionary algorithm, GOMORS, and propose \(\epsilon \)-GOMORS, i.e., a surrogate-based iterative evolutionary algorithm that combines Radial Basis Functions and \(\epsilon \)-dominance-based evolutionary search, to propose new points for expensive evaluations in each algorithm iteration. Moreover, a novel connected restart mechanism is introduced to ensure that the optimization search does not get stuck in locally optimum fronts. \(\epsilon \)-GOMORS is applied to a few benchmark multi-objective problems and a watershed calibration problem, and compared against GOMORS, ParEGO, NSGA-III, Borg, \(\epsilon \)-NSGA-II and MOEA/D on a limited budget of 1000 evaluations. Results indicate that \(\epsilon \)-GOMORS converges more quickly than other algorithms and the variance of its performance across multiple trials, is also less than other algorithms.


Expensive optimization Many objectives Meta-models 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Taimoor Akhtar
    • 1
    Email author
  • Christine A. Shoemaker
    • 2
    • 3
  • Wenyu Wang
    • 2
  1. 1.Environmental Research Institute, National University of SingaporeSingaporeSingapore
  2. 2.Department of Industrial Systems Engineering and ManagementNational University of SingaporeSingaporeSingapore
  3. 3.Department of Civil and Environmental EngineeringNational University of SingaporeSingaporeSingapore

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