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  1. Swarm Intelligence
  2. Swarm Intelligence : Volume 6
  3. Swarm Intelligence : Volume 6, Issue 1, March 2012
  4. Continuous optimization algorithms for tuning real and integer parameters of swarm intelligence algorithms
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Swarm Intelligence : Volume 11
Swarm Intelligence : Volume 10
Swarm Intelligence : Volume 9
Swarm Intelligence : Volume 8
Swarm Intelligence : Volume 7
Swarm Intelligence : Volume 6
Swarm Intelligence : Volume 6, Issue 4, December 2012
Swarm Intelligence : Volume 6, Issue 3, September 2012
Swarm Intelligence : Volume 6, Issue 2, June 2012
Swarm Intelligence : Volume 6, Issue 1, March 2012
Theoretical analysis of two ACO approaches for the traveling salesman problem
A critical analysis of parameter adaptation in ant colony optimization
Continuous optimization algorithms for tuning real and integer parameters of swarm intelligence algorithms
Swarm Intelligence : Volume 5
Swarm Intelligence : Volume 4
Swarm Intelligence : Volume 3
Swarm Intelligence : Volume 2
Swarm Intelligence : Volume 1

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

Content Provider SpringerLink
Author Yuan, Zhi Montes de Oca, Marco A. Birattari, Mauro Stützle, Thomas
Copyright Year 2011
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.
Starting Page 49
Ending Page 75
Page Count 27
File Format PDF
ISSN 19353812
Journal Swarm Intelligence
Volume Number 6
Issue Number 1
e-ISSN 19353820
Language English
Publisher Springer US
Publisher Date 2011-12-15
Publisher Place Boston
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Automated algorithm configuration Parameter tuning Continuous optimization algorithm Swarm intelligence F-Race ApplicationMathematics/Computational Methods of Engineering Artificial Intelligence (incl. Robotics) Communications Engineering, Networks Computer Communication Networks Computer Systems Organization and Communication Networks
Content Type Text
Resource Type Article
Subject Artificial Intelligence
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