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Multi-objective algorithm configuration (AAC) using a native multi-objective approach yields superior results compared to single-objective methods. This study demonstrates improved performance for complex optimization problems by optimizing multiple indicators simultaneously.

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Algorithm configurationlocal searchmulti-objective optimisationpermutation flowshop scheduling problemtravelling salesman problem.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Optimization

Background:

  • Automatic Algorithm Configuration (AAC) is crucial for high-performance solvers in complex optimization.
  • Existing AAC methods typically optimize a single performance metric for single-objective algorithms.
  • Applying single-objective AAC to multi-objective algorithms can be suboptimal.

Purpose of the Study:

  • To investigate the effectiveness of native multi-objective AAC procedures.
  • To compare single-objective AAC with a genuinely multi-objective AAC approach.
  • To determine the optimal AAC strategy for multi-objective optimization problems.

Main Methods:

  • Compared three AAC approaches: single-metric (hypervolume), weighted sum (hypervolume and spread), and multi-objective optimization.
  • Applied AAC methods to a local search framework for bi-objective permutation flowshop and traveling salesman problems.
  • Evaluated performance using hypervolume and spread indicators.

Main Results:

  • The multi-objective AAC approach significantly outperformed single-objective methods.
  • Optimizing complementary indicators simultaneously led to better algorithm performance.
  • Native multi-objective configuration is superior for multi-objective algorithms.

Conclusions:

  • Multi-objective optimization problems benefit from native multi-objective configuration procedures.
  • Single-objective AAC methods are insufficient for effectively configuring multi-objective algorithms.
  • The proposed multi-objective AAC approach enhances solver performance for complex problems.