Introduction

Computational Swarm Intelligence algorithms, often inspired by communication and interaction between social agents such as ants or bees, play important role in Artificial Intelligence. Different learning and adaptive mechanisms incorporated in these techniques (applied in real-world applications), are the research area that currently becomes the main field of Computational Collective Intelligence.

Topics of interests

The session will gather experts in adjacent and seemingly related fields of:

• Ant Colony Optimization
• Artificial Life
• Artificial Bee Colony
• Particle Swarm Optimization
• Multi-agent systems
• Differential Evolution
• Simulated Annealing
• Information Theoretic Learning
• Social Networks.

This session proposes that Computational Swarm intelligence is an autonomous aggregate of techniques that so far have not been unified, especially in the context of efficient applications. We are looking for a mathematical, algorithmic framework which will enable us to understand and analyze these algorithms and the self-adaptive mechanisms and learning schemas. The session will seek to define the metaheuristics of Computational Swarm Intelligence algorithms. A common framework is desirable for a number of reasons, including the following:

• Better understanding of the learning algorithms employed for different tasks of data mining and optimization in Computational Swarm Intelligence techniques.
• Discovering the relationships in data and the interactions between parts of the analyzed approaches.
• Suggestions for creating novel and hybrid metaheuristics in parallel implementations as well as in new applications.

The session aims at addressing such issues from a heuristic, practical and theoretical perspectives. The following contributions are welcomed:

• Position papers and reports of work in progress.
• Papers proposing and advancing the metaheuristics of popular swarm intelligence techniques such as PSO, ACO, BCO and DE.
• Contributions from adjacent fields e.g Social Networks, Multi-Agent Systems, Computational Learning Models.

Information for authors

The submitted papers should present results of the original and unpublished research. The papers will be reviewed by the ICCCI 2011 International Program Committee. The best submissions will be selected for presentation and will be included in the conference proceedings. The conference proceedings are planned to be published and distributed by Springer-Verlag in series LNCS/LNAI. Submitted papers should be prepared in LNCS/LNAI style and should be limited to 10 pages. All papers must be submitted electronically via conference submission system.

Important dates

Submission of papers:	15 March 2011   31 March 2011
Notification of acceptance:	30 April 2011
Camera ready papers submission:	15 May 2011

Organizers

Dr. hab. Urszula Boryczka
Institute of Computer Science, University of Silesia, Sosnowiec, Poland
e-mail: urszula.boryczka@us.edu.pl

Dr. hab. ing. Mariusz Boryczka
Institute of Computer Science, University of Silesia, Sosnowiec, Poland
e-mail: mariusz.boryczka@us.edu.pl

Dr. Marcin Budka
Smart Technology Research Centre, Computational Intelligence Research Group, School of Design, Engineering & Computing, Bournemouth University, Poole, United Kingdom
e-mail: mbudka@gmail.com

Dr. Katarzyna Musiał
Smart Technology Research Centre, Computational Intelligence Research Group, School of Design, Engineering & Computing, Bournemouth University, Poole, United Kingdom
e-mail: kmusial@bournemouth.ac.uk

Dr. ing. Radomil Matoušek

Institute of Automation and Computer Science, Dept. of Applied Computer Science, University of Technology Brno, Czech Republic

E-mail:  matousek@fme.vutbr.cz