Dr. Yuhui Shi (EDS-Engineering and Manufacturing Services, USA)

Prof. Russ Eberhart and Xiaohui Hu (Purdue School of Engineering and Technology Indianapolis, USA)

Dr. Xiaodong Li (RMIT Unviersity, Australia)

Dr. Hussein A. Abbass (Australian Defence Force Academy, Australia)

Important dates (Updated 6 Feb)

Submission:  February 10, 2004
Notification:   March 2, 2004
Camera-Ready:  March 21, 2004
Conference:   June 20-23, 2004

Swarm Intelligence (SI) is an Artificial Intelligence technique involving the study of collective behaviour in decentralized systems. Such systems are made up by a population of simple agents interacting locally with one other and with their environment.  Although there is typically no centralized control dictating the behaviour of the agents, local interactions among the agents often cause a global pattern to emerge. Examples of systems like this can be found in nature, including ant colonies, bird flocking, animal herding, honey bees, bacteria, and many more. Swarm-like algorithms, such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), have already been applied successfully to solve real-world optimization problems in engineering and telecommunication.  SI models have many features in common with Evolutionary Algorithms. Like EA, SI models are population-based. The system is initialized with a population of individuals (i.e., potential solutions).  These individuals are then manipulated over many iteration steps by mimicking the social behaviour of insects or animals, in an effort to find the optima in the problem space.  Unlike EAs, SI models do not explicitly use evolutionary operators such as crossover and mutation. A potential solution simply ‘flies’ through the search space by modifying itself according to its past experience and its relationship with other individuals in the population and the environment.

This special session will highlight the latest development in this rapidly growing research area of Swarm Intelligence. Authors are invited to submit their original and unpublished work in the areas including (but not limited to) the following:

• Particle swarm optimization
• Ant colony optimization
• Artificial life
• Culture algorithm
• Ecologically inspired models
• Other nature-inspired computation techniques
• Multi-objective optimization
• Constrained optimization
• Scheduling
• Real world applications

Submission (Updated 21 January 2004)

A submission should not be over 8 pages. This includes all figures, tables, graphs, photos, and bibliography entries. Extra pages may be purchased at US$100 each. Special session papers are treated the same as regular conference papers, and will be included in the conference proceedings. Please double check the as-printed appearance of your paper before sending it.

Organizers:

Yuhui Shi (yuhui.shi@eds.com)
EDS-Engineering and Manufacturing Services 1401 E. Hoffer St.
Kokomo, IN 46902, USA

Russ Eberhart (reberhar@iupui.edu) and Xiaohui Hu (xhu2@iupui.edu)
Purdue School of Engineering and Technology Indianapolis, Indiana 46206, USA

Xiaodong Li (xiaodong@cs.rmit.edu.au)
School of Computer Science and Information Technology
RMIT University, VIC 3001, Melbourne, Australia

Hussein A. Abbass (abbass@cs.adfa.edu.au)
School of Computer Science
University College, Australian Defence Force Academy
University of New South Wales, Canberra, ACT 2600, Australia

Technical committee:

James Kennedy (US Department of Labour, USA)

Russ Eberhart (Purdue School of Engineering and Technology Indianapolis, USA)

Yuhui Shi (EDS-Engineering and Manufacturing Services, USA)

Jonathan Fieldsend (University of Exeter, UK)

Gerry Dozier (Auburn University, USA)

Konstantinos E. Parsopoulos (University of Patras, Greece)

Eleni Laskari (University of Patras, Greece)

Michael N. Vrahatis (University of Patras, Greece)

Hitoshi Iba (University of Tokyo,Japan)

George Magoulas (Brunel University, UK)

Marcus Randall (Bond University, Australia)

Xiaohui Hu (Purdue School of Engineering and Technology Indianapolis, USA)

Hussein A. Abbass (ADFA, UNSW, Australia)

Antony Iorio (RMIT University, Australia)
Xiaodong Li (RMIT University, Australia)