Swarm-based Optimization for Distributed Systems
Organizers:
Baijian Yang (Ball State University, USA),
Hai-Bin Duan (Beihang University, Beijing, P. R. China)
Abstract:
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:
• 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
• Identify the performances gains that swarm intelligence can achieve in distributed systems
• Identify the weaknesses, if any, when applying swarm intelligence in distributed systems
• How Swarm Intelligence algorithms compares to other resource management algorithms in the field of distributed systems
Baijian Yang is an Assistant Professor at the Department of Industry and Technology, Ball State University, USA. He got his Ph.D. (2002) in Computer Science and Engineering at Michigan State University, USA. He did his M. S. (1998) and B. S. (1995) in Industrial Automation at Tsinghua University, China). He has published in papers in several international conferences and international journalsj including Journal of High-Speed Networks and Journal of Computer Communications.
Hai-Bin Duan is an associate professor at School of Automation Science and Electrical Engineering, Beihang University, Beijing, P. R. China, and he is also a M. Sc. Supervisor of Beihang University. He got his Ph. D. in Control Theory and Control Engineering from Nanjing University of Aeronautics & Astronautics (NUAA), Nanjing, P. R. China. From 1996 to 2000, he worked as an associate engineer in China Aviation Motor Control System Institute, which located in Wuxi city, P. R. China. Hai-Bin Duan has published 57 papers in international and domestic journals and important international conferences. He also published one book on "Ant Colony Algorithms: Theory and Applications" in Science Press in 2005. He is member of the editorial board of several journals, e.g., International Journal of Computer Science (IJCS), International Journal of Intelligent Technology (IJT), International Journal of Computational Intelligence (IJCI), and International Journal of Information Technology (IJIT). His current main research interests include swarm intelligence and its application in control, soft computing, real-time control system, evolvable control hardware design and realization.
Swarm Optimization Algorithms and Applications in Dynamic Environments (SOAADE)
Organizers:
Xiaohui Cui (Oak Ridge National Laboratory, USA) ),
Thomas E. Potok (Oak Ridge National Laboratory, USA),
Adel S. Elmaghraby (University of Louisville, USA)
Abstract:
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 just the global extreme but to track its orbit through space as closely 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.
Possible topic areas include, but are certainly not limited to:
• Dynamic fitness functions in Particle Swarm Optimization
• Searching for robust optimal solutions
• Tracking moving optima by swarm algorithms
• Benchmark problems and performance measures
• Adaptation and learning in swarm systems
• Potential effect of dynamic environment
• Theoretical analysis
• Sophisticated real-world applications
Thomas E. Potok is the leader of the Applied Software Engineering Research Group at the Oak Ridge National Laboratory, where he manages a staff of 14 researchers. He is the principal investigator on a number intelligent software agent research projects. Prior to this he worked for 14 years at IBM's Software Solutions Laboratory in Research Triangle Park, North Carolina. Dr. Potok has a BS, MS, and Ph.D. in Computer Engineering all from North Carolina State University. He is an adjunct faculty member at the University of Tennessee. He has authored numerous publications, has filed five software patents, and organized several workshops.
Xiaohui Cui is a research associate staff in the Computational Sciences and Engineering Division at the Oak Ridge National Laboratory. His research interests include Multi-agent System, Swarm Intelligence, Collective Behavior Modeling and Simulation, Information Retrieval and Knowledge Discovery. Dr. Cui received his PhD in Computer Science and Engineering from the University of Louisville in 2004. Prior to this, he had served as the technical director of the network center at Wuhan Technology University of Surveying and Mapping in China for more than three years. He also obtained Certificates of Senior Engineer and Engineer at the same university. Dr. Cui has authored numerous publications in knowledge discovery and swarm intelligence. Dr. Cui's research focuses on developing and applying multi-agent frameworks and algorithms featured with Swarm Intelligence to meet multiple national crucial challenges.
Adel S. Elmaghraby is the Professor and Chair of Computer Engineering and Computer Science Department, University of Louisville, Louisville, Kentucky, USA. His research interests cover Multimedia and Virtual Reality Systems, Artificial Intelligence, Computer Modeling and Simulation, Performance Evaluation, Human-Machine Systems, Automation and Manufacturing. Professor Elmaghraby directed over 60 Masters and doctoral research theses. He is the author of hundreds of refereed papers. Professor Elmaghraby received his Ph.D. in Electrical and Computer Engineering from the University of Wisconsin Madison in 1982. He severed as elected board member of International Society of Computers and Their Applications (ISCA), steering committee member of PADS and PDCS. He is an Associate editor of ISCA Journal, was an associate editor for Simulation Magazine (SCS), and founding editor of the joint IEEE-CS/ACM SIGSIM Simulation Digest. In 1996, he was awarded as the Golden Core member of the IEEE-Computer Society.
Swarm Intelligence and Computational Biology
Organizers:
Daniel Merkle (University of Leipzig, Germany),
Christian Blum (Universitat Politècnica de Catalunya, Spain)
Abstract:
Swarm intelligence methods are inspired by the collective behavior of individuals in decentralized, self-organized systems. They often provide state-of-the-art solutions for hard optimization problems. In the field of bioinformatics and computational biology such problems often occur. Successful optimization methods play a crucial role for finding optimal or near optimal solutions for such problems. Furthermore, swarm intelligence can be successfully applied for making predictions of real biological processes and structures. The goal of this special session is to present recent research results with the focus on combinations of swarm intelligence methods and computational biology. Contributions should address the combination of swarm intelligence inspired methods (for example particle swarm optimization or ant colony optimization, but also related optimization methods) and a research topic from computational biology.
These topics include, but are not limited to:
• Proteomics -Protein-ligand docking and inter-protein interaction
• Sequence alignment and sequence search
• Phylogeny and cophylogeny
• Drug discovery and drug design
• Fitness landscapes for biological systems
• Genomics
• Identification and classification of genes
• Gene expression
• Metabolic networks
• Bioinformatic databases
• Biomedical imagery
Daniel Merkle received the Diploma degree in Computer Science in 1997 at the University of Karlsruhe, Germany, in 1997. He finished his Ph.D. in applied computer science at the Institute of Applied Informatics and Formal Description Methods, University of Karlsruhe, Germany in 2002. Since then he works as a postdoctoral researcher with the Parallel Computing and Complex Systems Group of the Department of Computer Science in Leipzig, Germany. His research interests include phylogeny, parallel algorithms, algorithms from nature and social insect behaviour.
Christian Blum received the Diploma degree in mathematics in 1998 from the Universität Kaiserslautern (Germany), and the doctoral degree in applied sciences in 2004 from the Université Libre de Bruxelles (ULB, Belgium). He currently holds a postdoctoral fellowship at the ALBCOM research group of the Universitat Politècnica de Catalunya (UPC, Spain). Current subject of his research is the hybridization of ant colony optimization with more classical artificial intelligence and operations research methods. He co-organized several workshops and gave invited tutorial on ant colony optimization at the several conferences.
