Noiseless functions black-box optimization: evaluation of a hybrid particle swarm with differential operators

ABSTRACT

In this work we evaluate a Particle Swarm Optimizer hybridized with Differential Evolution and apply it to the Black-Box Optimization Benchmarking for noiseless functions (BBOB 2009). We have performed the complete procedure established in this special session dealing with noiseless functions with dimension: 2, 3, 5, 10, 20, and 40 variables. Our proposal obtained an accurate level of coverage rate, despite the simplicity of the model and the relatively small number of function evaluations used.

REFERENCES

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INDEX TERMS

Primary Classification:
  G. Mathematics of Computing
  G.1 NUMERICAL ANALYSIS
      G.1.6 Optimization
          Subjects: Global optimization

Additional Classification:
  G. Mathematics of Computing
  G.1 NUMERICAL ANALYSIS
      G.1.6 Optimization
          Subjects: Unconstrained optimization

General Terms:
Algorithms

Keywords:
benchmarking, black-box optimization, differential evolution, hybrid algorithms, noiseless functions, particle swarm