2016 IEEE Congress on Evolutionary Computation (IEEE CEC) Special Session on "Brain Storm Optimization Algorithms"

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 search potential may be obtained, while the capability learning focuses on its actually search from the current solution for single point based optimization algorithms and from the current population for population-based swarm intelligence algorithms.  The swarm intelligence algorithms with both capability learning and capacity developing can be called as developmental swarm intelligence algorithms.

The capacity developing is a top-level learning or macro-level learning methodology. The capacity developing describes the learning ability of an algorithm to adaptively change its parameters, structures, and/or its learning potential according to the search states of 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 describes the ability for an algorithm to find better solution(s) from current solution(s) with the learning capacity it possesses.

The Brain Storm Optimization (BSO) algorithm is a new kind of swarm intelligence, which is based on the collective behaviour of human being, that is, the brainstorming process. It is natural to expect that an optimization algorithm based on human collective behaviour could be a better optimization algorithm than existing swarm intelligence algorithms which are based on collective behaviour of simple insects, because human beings are social animals and are the most intelligent animals in the world. The designed optimization algorithm will naturally have the capability of both convergence and divergence.

The BSO algorithm is a good example of developmental swarm intelligence algorithm. A "good enough" optimum could be obtained through solution divergence and convergence in the search space. In the BSO algorithm, the solutions are clustered into several categories, and the new solutions are generated by the mutation of cluster or existing solutions. The capacity developing, i.e., the adaptation during the search, is another common feature of the BSO algorithms. 

The BSO algorithm can be seen as a combination of swarm intelligence and data mining techniques. Every individual in the brain storm optimization algorithm is not a solution to the problem to be optimized, but also a data point to reveal the landscapes of the problem. The swarm intelligence and data mining techniques can be combined to produce benefits above and beyond what either method could achieve alone.

Topics of Interest

This special session aims at presenting the latest developments of BSO algorithm, 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 algorithms are very welcome for this Special Session. Potential topics include, but are not limited to:

• Analysis and control of BSO parameters
• Parallelized and distributed realizations of BSO algorithms
• BSO for Multi-objective optimization
• BSO for Constrained optimization
• BSO for Discrete optimization
• BSO algorithm with data mining techniques
• BSO in uncertain environments
• Theoretical aspects of BSO algorithm
• BSO for Real-world applications

Submission

Please follow the IEEE CEC2016 instruction for authors and submit your paper via the IEEE CEC 2016 online submission system. Please specify that your paper is for the Special Session on Brain Storm Optimization Algorithms.

Important Dates

Paper Submission Deadline:    15 Jan 2016
Notification of Acceptance:    15 Mar 2016
Final Paper Submission Deadline:    15 Apr 2016

Organisers

Shi Cheng, University of Nottingham Ningbo, China, shi.cheng@nottingham.edu.cn
Quande Qin, Shenzhen University, Shenzhen China, qdqin@szu.edu.cn
Yuhui Shi, Xi'an Jiaotong-Liverpool University, Suzhou China, yuhui.shi@xjtlu.edu.cn
Simone Ludwig, North Dakota State University, USA, simone.ludwig@ndsu.edu

Committee Member

Shangce Gao, University of Toyama, Gofuku, Japan, gaosc@eng.u-toyama.ac.jp
Xingquan Zuo, Beijing University of Posts and Telecommunications, Beijing, China, zuoxq@bupt.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. 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.

Simone Ludwig received the PhD degree from Brunel University, UK, in 2004. She is currently an associate professor at North Dakota State University, USA, conducting research in the area of computational intelligence. In particular, developing novel algorithms to address different optimization problems in the area of data mining (large data) and distributed computing. She has published around 90 peer-reviewed articles both in journals and conference proceedings. Dr. Ludwig has served as a co-chair, track chair, and tutorial chair for different conferences, as well as served on numerous conference program committees. In addition, she currently serves on the editorial board of 3 journals.

Shangce Gao received the B.S. degree from Southeast University, Nanjing, China in 2005, and the M.S. and Ph. D. degrees in intellectual information systems and innovative life science from University of Toyama, Toyama, Japan in 2008 and 2011, respectively. He is currently an Associate Professor with the Faculty of Engineering, University of Toyama, Toyama, Japan. From 2011 to 2012, he was an associate research fellow with the Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai, China. From 2012 to 2014, he was an associate professor with the College of Information Sciences and Technology, Donghua University, Shanghai, China. His main research interests include computational intelligence, nature-inspired technologies, swarm intelligence, and neural networks for optimization and real-world applications. He was a recipient of the Shanghai Rising-Star Scientist award, the Chen-Guang Scholar of Shanghai award, the Outstanding Academic Performance Award of IEICE, the Outstanding Self-financed Students Abroad Award of Chinese Government, and the Outstanding Academic Achievement Award of IPSJ.

Xingquan Zuo received the Ph.D. degree in Control Theory and Control Engineering from Harbin Institute of Technology, Harbin, China, in 2004. He is currently an Associate Professor in Computer School, Beijing University of Posts and Telecommunications, Beijing, China. From 2012 to 2013, he was a Visiting Scholar in Industrial and System Engineering Department, Auburn University, AL, USA. His research interests are in intelligent optimization and scheduling, evolutionary computation, data mining with applications. He has published more than 70 research papers in journals and conferences, two books and several book chapters. He is a senior member of IEEE and served in program committee of many conferences.