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On the Design of Metaheuristics-Based Algorithm Portfolios

In: Open Problems in Optimization and Data Analysis

Author

Listed:
  • Dimitris Souravlias

    (Helmut-Schmidt University)

  • Konstantinos E. Parsopoulos

    (University of Ioannina)

Abstract

Metaheuristic optimization has been long established as a promising alternative to classical optimization approaches. However, the selection of a specific metaheuristic algorithm for solving a given problem constitutes an impactful decision. This can be attributed to possible performance fluctuations of the metaheuristic during its application either on a single problem or on different instances of a specific problem type. Algorithm portfolios offer an alternative where, instead of using a single solver, a number of different solvers or variants of one solver are concurrently or interchangeably used to tackle the problem at hand by sharing the available computational resources. The design of algorithm portfolios requires a number of decisions from the practitioner’s side. The present chapter exposes the essential open problems related to the design of algorithm portfolios, namely the selection of constituent algorithms, resource allocation schemes, interaction among the algorithms, and parallelism issues. Recent research trends relevant to these issues are presented, offering motivation for further elaboration.

Suggested Citation

  • Dimitris Souravlias & Konstantinos E. Parsopoulos, 2018. "On the Design of Metaheuristics-Based Algorithm Portfolios," Springer Optimization and Its Applications, in: Panos M. Pardalos & Athanasios Migdalas (ed.), Open Problems in Optimization and Data Analysis, pages 271-284, Springer.
    • Handle: RePEc:spr:spochp:978-3-319-99142-9_14
    DOI: 10.1007/978-3-319-99142-9_14
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