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  1. Swarm Intelligence
  2. Swarm Intelligence : Volume 7
  3. Swarm Intelligence : Volume 7, Issue 4, December 2013
  4. Artificial bee colonies for continuous optimization: Experimental analysis and improvements
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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 7, Issue 4, December 2013
An ant colony system for transportation user equilibrium analysis in congested networks
A nonlocal kinetic model for predator–prey interactions
Trail traffic flow prediction by contact frequency among individual ants
Artificial bee colonies for continuous optimization: Experimental analysis and improvements
Swarm Intelligence : Volume 7, Issue 2-3, September 2013
Swarm Intelligence : Volume 7, Issue 1, March 2013
Swarm Intelligence : Volume 6
Swarm Intelligence : Volume 5
Swarm Intelligence : Volume 4
Swarm Intelligence : Volume 3
Swarm Intelligence : Volume 2
Swarm Intelligence : Volume 1

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Artificial bee colonies for continuous optimization: Experimental analysis and improvements

Content Provider SpringerLink
Author Liao, Tianjun Aydın, Doğan Stützle, Thomas
Copyright Year 2013
Abstract The artificial bee colony (ABC) algorithm is a recent class of swarm intelligence algorithms that is loosely inspired by the foraging behavior of honeybee swarms. It was introduced in 2005 using continuous optimization problems as an example application. Similar to what has happened with other swarm intelligence techniques, after the initial proposal, several researchers have studied variants of the original algorithm. Unfortunately, often these variants have been tested under different experimental conditions and different fine-tuning efforts for the algorithm parameters. In this article, we review various variants of the original ABC algorithm and experimentally study nine ABC algorithms under two settings: either using the original parameter settings as proposed by the authors, or using an automatic algorithm configuration tool using a same tuning effort for each algorithm. We also study the effect of adding local search to the ABC algorithms. Our experimental results show that local search can improve considerably the performance of several ABC variants and that it reduces strongly the performance differences between the studied ABC variants. We also show that the best ABC variants are competitive with recent state-of-the-art algorithms on the benchmark set we used, which establishes ABC algorithms as serious competitors in continuous optimization.
Starting Page 327
Ending Page 356
Page Count 30
File Format PDF
ISSN 19353812
Journal Swarm Intelligence
Volume Number 7
Issue Number 4
e-ISSN 19353820
Language English
Publisher Springer US
Publisher Date 2013-10-08
Publisher Place Boston
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Artificial bee colony Continuous optimization Experimental study Automatic algorithm configuration Artificial Intelligence (incl. Robotics) Computer Systems Organization and Communication Networks ApplicationMathematics/Computational Methods of Engineering Communications Engineering, Networks Computer Communication Networks
Content Type Text
Resource Type Article
Subject Artificial Intelligence
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