Friday, 14 November 2014

Call for Papers CEC2015 Special Session "Developmental Swarm Intelligence"

Special Session for IEEE CEC 2015.

Overview

Swarm intelligence algorithm should have two kinds of ability: capability learning and capacity developing. The Capacity Developing focuses on moving the algorithm’s search to the area(s) where higher searching potential may be possessed, while the capability learning focuses on its actually searching from the current solution for single point based optimization algorithms and from the current population for population-based swarm intelligence algorithms.

The capacity developing is a top-level learning or macro-level learning. The capacity developing is the learning ability of an algorithm to adaptively change its parameters, structures, and/or its learning potential according to the search states on the problem to be solved. In other words, the capacity developing is the search strength possessed by an algorithm. The capability learning is a bottom-level learning or micro-level learning. The capability learning is the ability for an algorithm to find better solution(s) from current solution(s) with the learning capacity it is possessing.

The brain storm optimization (BSO) algorithm and Fireworks algorithm (FWA) are two good examples of developmental swarm intelligence (DSI) algorithms. The “good enough” optimum could be obtained through the solutions divergence and convergence in the search space. In BSO algorithm, the solutions are clustered into several categories, and the new solutions are generated by the mutation of cluster or existed solutions. While in FWA algorithm, mimicked by the fireworks exploration, the new solutions are generated by the exploration of existed solutions. The capacity developing, i.e., the adaptation in search, is another common feature in these two algorithms.

Topics of Interest

This special session aims at presenting the latest developments of developmental swarm intelligence algorithms, as well as exchanging new ideas and discussing the future directions of developmental swarm intelligence. Original contributions that provide novel theories, frameworks, and applications to developmental swarm intelligence are very welcome for this Special Session. Potential topics include, but are not limited to:
  • Analysis and control of DSI parameters
  • Parallelized and distributed realizations of DSI algorithms
  • DSI for Multi-objective optimization
  • DSI for Constrained optimization
  • DSI for Discrete optimization
  • DSI in uncertain environments
  • Theoretical aspects of DSI algorithm
  • DSI for Real-world applications

Submission

Please follow the IEEE CEC2015 instruction for authors and submit your paper via the IEEE CEC 2015 online submission system. Please specify that your paper is for the Special Session on Developmental Swarm Intelligence.

Important Dates

Paper Submission Deadline:          December 19, 2014
Notification of Acceptance:             February 19, 2015
Final Paper Submission Deadline:  March 12, 2015

Organisers

Shi Cheng, University of Nottingham Ningbo, China, shi.cheng-at-nottingham.edu.cn
Quande Qin, Shenzhen University, Shenzhen China, qdqin-at-szu.edu.cn
Yuhui Shi, Xi'an Jiaotong-Liverpool University, Suzhou China, yuhui.shi-at-xjtlu.edu.cn

Biography of the Proposers

Shi Cheng received the Bachelor's degree in Mechanical and Electrical Engineering from Xiamen University, Xiamen, the Master's degree in Software Engineering from Beihang University (BUAA), Beijing, China, the Ph.D. degree in Electrical Engineering and Electronics from Liverpool University, Liverpool, United Kingdom, the Ph.D. degree in Electrical and Electronic Engineering from Xi’an Jiaotong-Liverpool University, Suzhou, China in 2005, 2008, and 2013, respectively. He is currently a research fellow with Division of Computer Science, University of Nottingham Ningbo, China. He has published more than 30 research articles in peer-reviewed journals and international conferences. His current research interests include swarm intelligence, multiobjective optimization, and data mining techniques and their applications.

Quande Qin received PhD degree in Management Science and Engineering from School of Business Administration, South China University of Technology, Guangzhou, China. Currently, he is a lecturer in the College of Management, Shenzhen University, Shenzhen, China. His current research interests include swarm intelligence, evolutionary optimization and their applications in management and economics.

Yuhui Shi received the PhD degree in electronic engineering from Southeast University, Nanjing, China, in 1992. He is a Professor in the Department of Electrical and Electronic Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, China. Before joining Xi'an Jiaotong-Liverpool University, he was with Electronic Data Systems Corporation, Indianapolis, IN, USA. His main research interests include the areas of computational intelligence techniques (including swarm intelligence) and their applications. Dr. Shi is the Editor-in-Chief of the International Journal of Swarm Intelligence Research.

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