Special Sessions
- Swarm Algorithms in Control Systems
- Swarm-based Optimization for Distributed Systems
- Swarm Intelligence in Bioinformatics
- Applications of Swarm Intelligence to Power Systems
- Swarm-based Hybrid Systems
- Human Intelligence Interacting with Swarm Intelligence
- Swarm Intelligence and Discrete Mathematics
- Swarm Optimization Algorithms and Applications in Dynamic Environments
- Cultural Algorithms and Socially Motivated Optimization in Complex Systems
Swarm Algorithms in Control Systems
Renato Krohling
Chair for Control Systems
Department of Electrical Engineering, University of Dortmund
D-44221 Dortmund, Germany
http://esr.e-technik.uni-dortmund.de/krohling.htm
In this special session,
we seek papers on applications of Swarm algorithms to challenging problems in design and tuning of control systems. Important issues in advanced control systems are autonomy, adaptability, robustness and fault-tolerance. We are looking for original contributions on new developments in Swarm algorithms to solve these problems. Approaches using Co-evolution,multi-objectives, and parallel implementations are encouraged.
Topics include (but are not limited to)
- Robust, Adaptive, and Optimal Control
- Design and Tuning Methodologies for Control Systems
- Control Applications
- Emerging Control Technologies
- Modeling, Estimation, Identification and Optimization
- Process Control
- Power Systems
- Real-time Control
- Fault Detection
- Robotics and Mechatronics, eg., Swarm Robotics
- Guidance and Flight Control
- Vehicles and Transportation Systems
- Hybrid and Complex Systems
Swarm-based Optimization for Distributed Systems
Xiaowu Chen
School of Computer Science and Engineering, Beihang
University, Beijing, China, 100083
Baijian Yang
Dept. of Industry and Technology, Ball State University,
Munice, IN, U.S.A 47306
Distributed systems have been a very active research field for a number of decades. The momentum is still growing with the widely deployed broadband networking techniques, which prompt advances made in areas including grid computing, peer-to-peer sharing, and multicasting.
One of the most challenging problems in the field of distributed systems, namely how to manage system resources in a distributed system to maximize utilization and efficiency, remains to be an active research area, despite the vast number of past research done in the field. The ever popular Swarm-based Optimization algorithms, such as Particle Swarm Optimization and Ant Colony algorithms, certainly shed light into this application area.
The primary goals of the proposed Distributed systems special session include, but are not limited to:
1) How swarm intelligence can be applied in resource management in a distributed system, such as a grid systems, a peer-to-peer systems, or a multicast systems
2) Identify the performances gains that swarm intelligence can achieve in distributed systems
3) Identify the weaknesses, if any, when applying swarm intelligence in distributed systems
4) How Swarm Intelligence algorithms compares to other resource management algorithms in the field of distributed systems
Swarm Intelligence in Bioinformatics
Nelis Franken
Bryton S. Masiye
Department of Computer Science, University of Pretoria,
Pretoria 0002, South Africa
Phone: +27 12 420 4116 or +27 12 420 3649
The social behaviour of insects has been of great interest to many researchers,
giving rise to a number of interesting algorithms, such as Particle Swarms
Optimisation and Ant Colony Optimisation amongst others. Of equal interest is
the implementation and application of such techniques to various problem areas.
Bioinformatics is the formulation and application of computing approaches in the
analysis, modeling and mining of biological data. A special symbiotic
relationship between swarm inspired techniques and Bioinformatics has formed
over recent years, and it is the aim of this special session to focus attention
on the efforts of research in this vibrant pursuit.
The special session hopes to attract contributions from, but not limited to, the
following topics with special focus on swarm intelligence applications:
- Protein Function Prediction
- Sequence alignment and consensus
- Molecular Docking
- Gene Array Analysis
- Proteome Analysis
- Structure Modelling and Prediction
- Visualisation
Applications of Swarm Intelligence to Power Systems
Mohamed A. El-Sharkawi
Professor of Electrical Engineering
Department of Electrical Engineering,
University of Washington
Campus box: 352500,
Seattle, WA 98195-2500
Phone: (206) 685-2286
Fax: (206) 543-3842
web: http://cialab.ee.washington.edu
The special session deals with the application of swarm intelligence to power
systems. Swarm intelligence has been successfully implemented in several
applications such as security classification, control, protection, optimal
dispatch and reactive power compensation. In addition to these applications,
swarm intelligence has opened the door to the implementation of multi-objective
optimization and Pareto optimization. The participants will describe the
paradigm and theory of several power system applications. The participants will
highlight the significant role and practicality of the swarm intelligence by the
comparative analysis of several power system applications.
Swarm-based Hybrid Systems
K.K. Aggarwal
Vice-Chancellor
GGS Indraprastha
University Delhi 110006, INDIA
Shakti Kumar
Director
Center for Advanced Technologies, Haryana Engineering
College Jagadhari 135003, INDIA
Arun Khosla
Sr Lecturer
Department of Electronics and Communication
Engineering, National Institute of Technology Jalandhar 144 011 INDIA
Corresponding organizer: Arun Khosla,
khoslaak@nitj.ac.in,
arun.khosla@gmail.com
Phone: +91-181-2690301, 2690302 Ext. 364 (Office)
+91-9888068332 (Mobile)
Fax: +91-181-2690320
Different techniques are
being used for solving real-world problems. Each specific technique has its
particular strengths and weaknesses that make it suitable for certain situations
and not effective for others. These limitations have been a central driving
force behind the creation of hybrid systems, where two or more techniques are
combined in a manner to mitigate the limitations and take advantages of the
strengths to produce systems that are more effective and powerful than those
could be built with single technique.
Swarm algorithms such as Particle Swarm Optimization, Ant Colony Optimization and others have been emerging as an innovative and powerful computational metaphor for solving complex problems in design, optimization, control, management, business and finance. These algorithms lay emphasis on methodology that features autonomy, emergence and distributed computing and the solutions based on this framework have been found to be more robust, flexible and adaptable.
The aim of this special session is to advocate the hybridization of swarm-based systems with other techniques. It is expected that the prospective contributions are unpublished and present novel, fundamental research offering innovative contributions either from a methodological viewpoint or from an application perspective based on hybrid approaches, where swarm-based algorithm is one of the constituent technique.
Topics of interest include but not limited to:
- Hybridization of Swarm-based algorithms with Fuzzy Logic, Neural Networks and Genetic Algorithms
- Swarm-based Multi-Agent Systems
- Hybridization of Swarm systems with Rough Sets, Bayesian Networks
- Application of Swarm-based hybrid systems in Signal and Image Processing, Control, Medical Diagnosis, Data Mining
- Application of Swarm-based hybrid systems in Business, Finance and Management
Human Intelligence
Interacting with Swarm Intelligence
Marc Kirschenbaum and
Daniel Palmer
Department of Mathematics and Computer Science,
John Carroll University
Cleveland, OH 44118, USA
Ravi Vaidyanathan
Department of Systems Engineering,
Naval Postgraduate School, Monterey, CA 93940, USA
Many research efforts have combined these two areas in interesting ways. Farkas,
Helbing and Vicsek are studying humans as a physical media for wave propagation
at Mexican sporting events, Bonabeau, Fumes, and Orme used human decision-making
as the fitness function for a genetic algorithm to develop stable patterns in
swarms (which were then enacted by human agents), the third annual conference on
Pedestrian and Evacuation Dynamics (PED 2005) was held this past September with
30+ papers on modeling and simulating humans in emergency situations, and in his
forthcoming book, Swarm Creativity, Peter A. Gloor examines how humans,
interacting in networks can produce innovation. In this special session, we want
to encourage wide interpretation of the title and explore the many ways human
intelligence and swarm intelligence complement, support, or enhance each other.
Papers about smart mobs, and simulation of collections of humans are just as
appropriate as those doing evacuation dynamics and organizational human factors
studies. The goal of this session is to provide an environment in which a
collection of researchers who work with both humans and swarms can provide
inspiration and insight to each other. This is the beginning of a dialog that we
hope will lead to new synergies and collaborations.
Topics include:
- Human Swarms
- Smart Mobs
- Evacuation Dynamics
- Organizational Behavior
- Staffing Allocation
- Logistics Optimization
- Decentralized Management
- Crowd Dynamics
- Temporal Self-Organization
...and other areas of research that involve both humans and swarms
Swarm Intelligence and Discrete Mathematics
Michael N. Vrahatis
University of Patras, Greece
Ilias S. Kotsireas
Wilfrid Laurier
University, Canada
Konstantinos E. Parsopoulos
University of Patras, Greece
The rapidly emerging field of swarm intelligence has attracted substantial
attention from science and engineering researchers over the last few
years. Discrete Mathematics and Discrete Optimization, constitute an
important source of computationally hard problems that can be tackled through
swarm intelligence algorithms. Particle Swarm Optimization and Ant Colony
Optimization, the most common swarm intelligence paradigms, have already
been applied to problems of this sort.
The aim of this special session is to bring together researchers interested in
applications of swarm intelligence in discrete mathematical problems, as
well as the improvement of swarm intelligence algorithms through discrete
mathematical tools. The special session will feature papers dealing with
applications of swarm intelligence algorithms in all areas of discrete
mathematics including, but not limited to the following:
- discrete optimization
- clustering
- cryptography
- scheduling
- data mining
- graph theory
Swarm Optimization Algorithms and
Applications in Dynamic Environments
Xiaohui Cui and
Thomas E. Potok
Applied Software Engineering Research Group
Computational Sciences & Engineering Division
Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
Adel Elmaghraby
Computer Engineering and Computer Science Department
University of Louisville, Louisville, KY 40292, USA
In the real world we usually have to deal with the task of searching for an
optimal solution in a dynamic environment. Because of the continual change of
both the external environment and parameters, the optimum solution will also
change with time. In contrast to the static case, the main goal in dynamic
optimization problems is no longer to acquire the global extreme but to track
its orbit through the space as close as possible, or to find a robust solution
that operates optimally in the presence of uncertainties. Many algorithms fail
when applied to the dynamic problem due to their inability to adapt or
self-adapt to the change of the environment. The swarm algorithms have been
demonstrated as efficient algorithms for tracking/finding the optimal solution
in the dynamic environment and have received an increasing interest over the
past several years. The objective of this special session is to bring
researchers from academia and industry together to review the latest advances
and to explore future directions in the field.
Topics of interest include but are not limited to:
- Dynamic fitness functions in PSO
- Searching for robust optimal solutions
- Tracking moving optima
- Benchmark problems and performance measures
- Adaptation and learning
- Potential effect of Dynamic Environment
- Theoretical analysis
- Sophisticated real-world applications
Cultural Algorithms and Socially Motivated Optimization in Complex Systems
Robert G. Reynolds
Dept. of Computer Science
Wayne State University
Detroit, Michigan 48202
Phone: 313-577-0726
Fax: 313-577-6868
Ziad Kobti
University of Windsor
Windsor, Ontario, Canada
This special session will investigate applications of socially motivated learning via cultural algorithms to
various problems in in science, engineering, and technology.