Approved Special Sessions at ICSI 2014

No. Title
Session Chairs
Contact
1 Special Session on "Swarm Intelligence in Image and Video Processing"
2 Special Session on "Applications of Swarm Intelligence to Management Problems"

Dr. Quande Qin
Dr. Shi Cheng
Dr. Chenggang Cui

3 Special Session on " Swarm Intelligence for Real-world Application"
Dr. Boyang Qu
Dr. J. J. Liang
Dr. Quan-Ke Pan
Dr. P N Suganthan
4 Special Session on "The Integration of Swarm Intelligence and Data Mining"
Dr. Xinye Cai
Dr. Zhun Fan
Dr. Shi Cheng
Dr. Ke Tang
5 Special Session On "Swarm intelligence in Electrical Engineering Applications"

Dr. Kouzou Abdellah
Dr. Hafaifa Ahmled
Dr. Kaabeche Hamid
Dr. Said Diaf

 


1. Special Session on "Swarm Intelligence in Image and Video Processing"

Session Chair

Prof. Benlian Xu, Changshu Institute of Technology
Email: [email protected]

Aim and Scope
Swarm intelligence, as a scientific discipline, is born from biological insights about the incredible abilities of social insects to solve their everyday-life problems. Various algorithms have been developed to model such swarm systems, including ant colonies, schools of fish, colonies of honey bees, and so on. Their colonies display fascinating behaviors that combine efficiency with both flexibility and robust. The study of natural swarm intelligence leads directly to novel algorithms that have a wide variety of applications.
Image and video processing is a very important research area due to the rapid development of image sensors and visual communication techniques. Inspirations from swarm intelligence have been used in recent years to address challenging real-world image and video processing problems. There are a number of critical issues regarding the modeling of swarm evolutionary systems, the formulation of image and video processing problems, the design of efficient optimization/search algorithms, and so on. This special session aims to bring together new theories and methodologies inspired by swarm intelligence and address the broad challenges in both theoretical and application aspects of swarm intelligence in image and video processing.

*** Topics of interest (but are not limited to) ***
Methods
-Mathematical models of biological or social collective systems
-Particle swarm optimization
-Ant colony optimization
-Bee colony optimization
-Bacteria foraging
-Fish schooling
Applications
-Image/video processing and analysis, restoration, segmentation, object tracking
-Image/video modeling and representation
-Image/video processing in biomedical sciences, geosciences and remote sensing, document image processing
-other applications

Important Data: 
Initial Submission:                  April 01, 2014
Notification of Acceptance:       June 15, 2014
Final Paper Submission:            July 01, 2013

Paper submission: 
Prospective authors are invited to contribute high-quality papers (8-12 pages) to ICSI’2014 through Online Submission System or mail to [email protected]. All accepted Papers will be published in Springer’s Lecture Notes in Computer Science (indexed by EI, ISTP, DBLP, and ISI) 

Biography of Organizer:

Prof. Benlian Xu
Dean of School of Electric and Automatic Engineering
Changshu Institute of Technology
Changshu, 215500, China
Email: [email protected]


2. Special Session on "Applications of Swarm Intelligence to Management Problems"

Session Chairs

Dr. Quande Qin,  Shenzhen University
E- mail: [email protected]
Dr. Shi Cheng,  University of Nottingham Ningbo
E-mail: [email protected]
Dr. Chenggang Cui, Shanghai Advanced Research Institute, Chinese Academy of Sciences
E-mail: [email protected]

Aim and Scope
As a relative new branch of artificial intelligence, swarm intelligence is a powerful optimization technique that can be applied to a wide variety of real-world problems. Compared with traditional derivative-based optimization approach, swarm intelligence brings flexibility to problem solving, as no convenient assumptions are required, such as smooth functions or differentiability.

Due to many real management problems become increasingly complicated, the features of these problems such as highly-dimensional, non-differentiable, non-continuous, non-convex and multiple objectives pose severe challenges to the traditional optimization techniques, or even impossible. Recently, many researchers have resorted to swarm intelligence to handle these management problems. For example, finance portfolio selection problems involve very complex issues: risk, uncertainty and the existence of many complicated realistic constraints, such as cardinality constraint and bounding constraints. Unsurprisingly, swarm intelligence algorithms have been widely applied in this field.

The special session is focused on topics related applications of swarm intelligence to management optimization. Potential topics include, but are not limited to:

  • Manufacturing planning and control
  • Supply chain network management and design
  • Shop scheduling
  • Vehicle routing
  • Financial portfolio selection
  • Energy demand & consumption forecast
  • Regional energy planning
  • Facility layout design
  • Project portfolio management
  • Site location analysis
  • Capability investment management
  • Evacuation planning
  • Project scheduling
  • Resource allocation
  • Capital budget
  • Municipal solid waste management

3. Special Session on " Swarm Intelligence for Real-world Application"

Session Chairs

Dr. B. Y. Qu, Zhongyuan University of Technology
Email: [email protected]
Dr. J. J. Liang, Zhengzhou University
Email: [email protected]
Dr. Quan-Ke Pan, Liaocheng University and State Key Laboratory of Synthetical Automation for Process Industries (Northeastern University)
Email: [email protected]
Dr. P N Suganthan, Nanyang Technological University
Email: [email protected]

 

Aim and Scope
Swarm intelligence (SI) is a recent trend in computational intelligence and popular for the simplicity of its realizations. It is the collective behavior of decentralized, self-organized systems, natural or artificial. Various swarm intelligence based algorithms have been proposed in recent years such as particle swarm optimization, artificial bee colony optimization, artificial immune systems, glowworm swarm optimization, intelligent water drops, self-propelled particles, stochastic diffusion search, and etc. As optimization techniques, methods in swarm intelligence have been widely applied to many aspects in real-world application and have shown great success. This special session aims to promote the application of the swarm intelligence based algorithms and other meta-heuristics to solving real-world engineering optimization problems. This special session concentrates on the related topics of integrating and utilizing algorithms in swarm intelligence and real-world applications. It aims to promote the application of the swarm intelligence based algorithms and other meta-heuristics to solving real-world optimization problems. Topics of interest may cover, but are not limited to

  • Chemical engineering optimization
  • Examination timetabling problem
  • Structural design optimization
  • Multiple sequence alignment
  • Truss optimization
  • Production scheduling
  • Production planning
  • Path planning
  • Construction site layout optimization
  • Multi-objective design optimization
  • Nonlinear parameter estimation
  • Data mining

 

Biography of Organizers:

Dr. B. Y. Qu
Email: [email protected]
He received the B.E. degree and Ph.D. degree from the school of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, in 2007 and 2012, respectively. He is currently an Associate Professor in the School of Electric and Information Engineering, Zhongyuan University of Technology, China.  His research interests include evolutionary computation, swarm intelligence, multi-objective optimization and applications of evolutionary computation.
Dr. J. J. Liang
Email: [email protected]
She received the B. Eng. degree from Harbin Institute of Technology, China and the Ph.D. degree from the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. She is currently an Associate Professor in the School of Electrical Engineering, Zhengzhou University, China. She is an associate editor of the IEEE Computational Intelligence Magazine. Her main research interests are evolutionary computation, swarm intelligence, multi-objective optimization and applications of evolutionary computation.
Dr. Quan-Ke Pan
Email: [email protected]
He received the B.Sc. degree and the Ph.D. degree from Nanjing university of Aeronautics and Astronautics, Nanjing, China, in 1993 and 2003, respectively. Since 2003, he has been with School of Computer Science Department, Liaocheng University, where he became a Full Professor in 2006. He has been with State Key Laboratory of Synthetical Automation for Process Industries (Northeastern University) since 2011. His current research interests include intelligent optimization and scheduling. He has authored more than 200 refereed papers.
Dr. P N Suganthan (S’91–M’92–SM’00)
Email: [email protected]
He received the B.A. degree, postgraduate certificate, and the M.A. degree in electrical and information engineering from the University of Cambridge, UK, in 1990, 1992, and 1994, respectively, and the PhD degree from the School of EEE, NTU, Singapore. He was a predoctoral Research Assistant in the Department of Electrical Engineering, University of Sydney in 1995–96 and a lecturer in the Department of Computer Science and Electrical Engineering, University of Queensland in 1996–99. Since 1999 he has been with the School of EEE, NTU, Singapore. He is an associate editor of the IEEE Trans on Evolutionary Computation, Information Sciences, Pattern Recognition and Int. J. of Swarm Intelligence Research Journals. He is a founding co-editor-in-chief of Swarm and Evolutionary Computation, an Elsevier journal. SaDE (April 2009) paper won "IEEE Trans. on Evolutionary Computation" outstanding paper award. His former PhD student Dr Jane Jing Liang won the IEEE CIS Outstanding PhD dissertation award in 2014. His research interests include evolutionary computation, pattern recognition, multi-objective evolutionary algorithms, bioinformatics, applications of evolutionary computation and neural networks.
http://www.ntu.edu.sg/home/epnsugan/


4. Special Session on "The Integration of Swarm Intelligence and Data Mining"

Session Chairs:

Dr. Xinye Cai, Nanjing University of Aeronautics and Astronautics
E‐Mail: [email protected]
Dr. Zhun Fan, Shantou University
E‐mail: [email protected]
Dr. Shi Cheng, University of Nottingham Ningbo
E‐mail: [email protected]
Dr. Ke Tang, University of Science and Technology of China
E‐mail: [email protected]

Aim and Scope
Swarm Intelligence(SI), such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Differential Evolution (DE), Genetic Algorithm (GA) and the like, has been widely applied to many aspects in the fields of data mining(and/or machine learning), mostly as an optimization technique. On the other hand, SI is a class of population-based iterative algorithms, which generate abundant data about the search space, problem feature and population information during the optimization process. Therefore, the data mining (and/or machine learning) techniques can also be applied to analyze these data for improving the performance of SI. Many successful applications have been reported in the integration of swarm intelligence and data mining, yet, there remain many open issues and opportunities that are continually emerging as intriguing challenges for the field. This special session aims to bring together advancing theories, technologies and practice in the integration of swarm intelligence and data mining (and/or machine learning).

We invite researchers to submit their original and unpublished work related to, but not limited to, the following topics:

· SI Enhanced by Data Mining and Machine Learning Concepts and/or Method
· Data Mining and Machine Learning Based on SI techniques
· Data Mining and Machine Learning Enhanced Multi-bjective Optimization
· Data Mining and Machine Learning Enhanced Constrained Optimization:
· Data Mining and Machine Learning Enhanced Memetic Computation:
· Multi-Objective Optimization and Rule Mining Problems
· Knowledge Discovery in Data Mining via Swarm Intelligence
· Genetic Programming in Data Mining
· Multi-Agent Data Mining using Swarm Intelligence
· Medical Data Mining with Swarm Intelligence
· Swarm Intelligence in Intelligent Network Management
· Evolutionary Clustering in Noisy Data Sets
· Big Data Projects with Swarm Intelligence
· Real World Applications

Important Data:
Initial Submission: April 01, 2014
Notification of Acceptance: June 15, 2014
Final Paper Submission: July 01, 2013

Paper submission:
Prospective authors are invited to contribute high-quality papers (8-12 pages) to ICSI'2014 through Online Submission System by choosing Special Session on "The Integration of Swarm Intelligence and Data Mining". For more submission information please visit: http://www.ic-si.org/submission. All accepted Papers will be published in Springer's Lecture Notes in Computer Science (indexed by EI, ISTP, DBLP, and ISI)

Organizers

Dr. Xinye Cai, Nanjing University of Aeronautics and Astronautics
E‐Mail: [email protected]
Dr. Zhun Fan, Shantou University
E‐mail: [email protected]
Dr. Shi Cheng, University of Nottingham Ningbo
E‐mail: [email protected]
Dr. Ke Tang, University of Science and Technology of China
E‐mail: [email protected]


5. Special Session On "Swarm intelligence in Electrical Engineering Applications"

Session Chairs:

Dr. Kouzou Abdellah, Djelfa University, Algeria
Email: [email protected]
Dr. Hafaifa Ahmled, Djelfa University, Algeria
Email: [email protected]
Dr. Kaabeche Hamid, CDER Algiers, Algeria
Email: [email protected]
Dr. Said Diaf, CDER Algiers, Algeria
Email: [email protected]

Aim and Scope
In electrical engineering several algorithms have been developed and used to solve various control problems based on optimization algorithms under linear and nonlinear objective functions with and without constraints. Whereas these algorithms were using analytical expression that require essentially the gradient information and a good choice of the starting point to find the global optimum for a given model. However, nowadays with the industrial development, electrical engineering are facing plenty of very complex control problems based on optimization algorithms, sometimes with multi-optimum that are very difficult to be solved using these algorithms. An alternative solution to avoid the mentioned computational drawbacks is the use of new algorithms strategies based on swarm intelligence that are using combining rules and randomness to imitate almost the natural and physical phenomena of swarms behaviors in nature such as ants, bees, fiches, different flocks, etc., Indeed it was proved that some of the new proposed methods and the meta-heuristic proposed algorithms have overcome several deficiencies that were faced with the conventional numerical methods and they are still in swing to develop new algorithms or to hybrid existing algorithms to generate new more powerful control algorithms in industrial electrical engineering using natural or/and artificial phenomena based on solving the optimization problems.

Topics but not limited:
1- Swarm intelligence in Power system;
2- Swarm intelligence in energy management;
3- Swarm intelligence in Power Electronics Devices;
4- Swarm intelligence in Controllers;
5- Swarm intelligence in Renewable Energies;
6- Swarm intelligence in Electric Vehicles;
7- Swarm intelligence in Electrical Materials;
8- Swarm intelligence in Sensors;
9- Swarm intelligence in optimal control.

 

To be added more.


Call for Special Session Proposals

ICSI 2014 technical program will include special sessions. Their aim is to provide a complementary flavor to the regular sessions and should include hot topics of interest to the swarm intelligence community that may also go beyond disciplines traditionally represented at ICSI.

Prospective organizers of special sessions should submit proposals indicating:

* Title of the session.
* Rationale of the need for the special session at ICSI.
The rationale should stress the novelty of the topic and/or its multidisciplinary flavor, and must explain how it is different from the subjects covered by the regular sessions.
* Short biography of the organizers.
* List of 5 – 6 contributed papers (including titles, authors, contact information of the corresponding author) (this can also be provided later when they become available).

Proposals are due on or before March 15, 2014 and should be sent via e-mail (in either pdf or plain ASCII text form) to the special sessions chair (Dr. Shi Cheng at [email protected]) and forward to ICSI 2014 secretariat at [email protected].

Proposals will be evaluated based on the timeliness of the topic, the qualifications of the organizers and the authors of the papers proposed in the session. In its decision, the committee will try to realize a balance of the topics across the technical areas represented in swarm intelligence.

Notification of acceptance will be sent to the organizers no later than March 15, 2014. Authors of papers included in approved special sessions should submit their manuscript on or before April 1, 2014. Manuscripts should conform to the formatting and electronic submission guidelines of regular ICSI papers (Springer’s LNCS format).

When they submit papers, there is a choice to indicate that their papers are special session papers which will also undergo peer review. It is the responsibility of the organizers to ensure that their special session meets the ICSI quality standards. If, at the end of the review process, less than four papers are accepted, the session will be canceled and the accepted papers will be moved to regular sessions.

 


Last update: March 15, 2014.