Continuous optimization algorithms for tuning real and integer parameters of swarm intelligence algorithms

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...

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Veröffentlicht in:Swarm intelligence Jg. 6; H. 1; S. 49 - 75
Hauptverfasser: Yuan, Zhi, Montes de Oca, Marco A., Birattari, Mauro, Stützle, Thomas
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Boston Springer US 01.03.2012
Schlagworte:
Artificial Intelligence
Communications Engineering
Computer Communication Networks
Computer Science
Computer Systems Organization and Communication Networks
Mathematical and Computational Engineering
Networks
Swarm intelligence
F-Race
Automated algorithm configuration
Parameter tuning
Continuous optimization algorithm
ISSN:1935-3812, 1935-3820
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Zusammenfassung: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.
ISSN:1935-3812
1935-3820
DOI:10.1007/s11721-011-0065-9