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Parallel algorithm portfolios with adaptive resource allocation strategy


Algorithm portfolios are multi-algorithmic schemes that combine a number of solvers into a joint framework for solving global optimization problems. A crucial part of such schemes is the resource allocation process that is responsible for assigning computational resources to the constituent algorithms. We propose a resource allocation process based on adaptive decision-making procedures. The proposed approach is incorporated in algorithm portfolios composed of three essential types of numerical optimization algorithms, namely gradient-based, direct search, and swarm intelligence algorithms. The designed algorithm portfolios are experimentally demonstrated on a challenging optimization problem for different dimensions and experimental settings. The accompanying statistical analysis offers interesting conclusions and insights on the performance of the algorithm portfolio compared to its constituent algorithms, as well as on the effect of its parameters.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.


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This research is co-financed by Greece and the European Union (European Social Fund-ESF) through the Operational Programme “Human Resources Development, Education and Lifelong Learning” in the context of the project “Reinforcement of Postdoctoral Researchers-2nd Cycle” (MIS-5033021), implemented by the State Scholarships Foundation (IKY).

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Correspondence to Konstantinos E. Parsopoulos.

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Parsopoulos, K.E., Tatsis, V.A., Kotsireas, I.S. et al. Parallel algorithm portfolios with adaptive resource allocation strategy. J Glob Optim (2022).

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  • Algorithm portfolios
  • Adaptive pursuit
  • Resource allocation
  • Global optimization
  • Metaheuristics

Mathematics Subject Classification

  • 68T20
  • 68W20
  • 90C26