Recent Developments in Swarm Intelligence

A special issue of Information (ISSN 2078-2489).

Deadline for manuscript submissions: closed (28 February 2014) | Viewed by 1010

Special Issue Editor


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Guest Editor
Artificial Intelligence Research Institute (IIIA), Spanish National Research Council (CSIC), Campus of the UAB, 08193 Bellaterra-Catalonia, Spain
Interests: swarm intelligence; optimization; metaheuristics; hybrid algorithms

Special Issue Information

Dear Colleagues,

Swarm intelligence techniques are inspired by the collective behavior of individuals in decentralized, self-organized animal societies. Examples of such societies include ant colonies, flocks of birds, or fish schools. These societies are characterized by the constituent individuals’ relative lack of unsophistication. Nevertheless, in cooperation, these individuals are able to solve complex tasks. A prime example of this phenomenon is the ability of ants to find the shortest paths between their nest and food sources. The thriving force of task-solving is to be found in self-organization, which results from different forms of communication between the individuals. Especially important for swarm intelligence is indirect communication via changes applied to the environment (for example, by means of pheromone trails). This form of communication has been labeled “stigmergy”. Outstanding characteristics of swarm intelligence techniques include robustness and flexibility. Swarm intelligence techniques have been utilized by scientists in a multitude of applications, and particularly in optimization and the control of robots

The goal of this special issue is to provide the interested reader with a collection papers describing recent developments in swarm intelligence. Topics of interest include, but are not limited to:

* Swarm intelligence techniques in optimization, including:
+ Ant Colony Optimization
+ Particle Swarm Optimization
+ Bee Colony Optimization
+ Optimization algorithms inspired by the behavior of wasps, termites, fireflies
* Swarm intelligence applications to dynamic optimization problems
* Swarm intelligence techniques for multi-objective optimization
* Swarm intelligence techniques in control and robotics
* Swarm intelligence applied in decentralized settings
* Hybridization of swarm intelligence techniques with other methods
* Theoretical results

Prof. Dr. Christian Blum
Guest Editor

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Keywords

  • swarm intelligence
  • optimization
  • combinatorial optimization
  • real-valued optimization
  • distributed computing
  • algorithms
  • hybrid algorithms
  • ant colony optimization
  • particle swarm optimization
  • bee colony optimization
  • multi-objective optimization
  • swarm robotics

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Published Papers

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