Continuous optimization algorithms for tuning real and integer parameters of swarm intelligence algorithms |
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Authors: | Zhi Yuan Marco A Montes?de?Oca Mauro Birattari Thomas Stützle |
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Institution: | (1) IRIDIA, CoDE, Universit? Libre de Bruxelles, Brussels, Belgium;(2) Department of Computer Engineering, Ege University, Izmir, Turkey |
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Abstract: | The performance of optimization algorithms, including those based on swarm intelligence, depends on the values assigned to
their parameters. To obtain high performance, these parameters must be fine-tuned. Since many parameters can take real values
or integer values from a large domain, it is often possible to treat the tuning problem as a continuous optimization problem.
In this article, we study the performance of a number of prominent continuous optimization algorithms for parameter tuning
using various case studies from the swarm intelligence literature. The continuous optimization algorithms that we study are
enhanced to handle the stochastic nature of the tuning problem. In particular, we introduce a new post-selection mechanism
that uses F-Race in the final phase of the tuning process to select the best among elite parameter configurations. We also
examine the parameter space of the swarm intelligence algorithms that we consider in our study, and we show that by fine-tuning
their parameters one can obtain substantial improvements over default configurations. |
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