Special Session on

Large Scale Global Optimization


In the past two decades, many nature-inspired optimization algorithms have been developed and applied successfully for solving a wide range of optimization problems, including Simulated Annealing (SA), Evolutionary Algorithms (EAs), Differential Evolution (DE), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Estimation of Distribution Algorithms (EDA), etc. Although these techniques have shown excellent search capabilities when applying to small or medium sized problems, they still encounter serious challenges when applying to large scale problems, i.e., problems with several hundreds to thousands of variables. The reasons appear to be two-fold. Firstly, the complexity of a problem usually increases with the increasing number of decision variables, constraints, or objectives (for multi-objective optimization problems). Problems with this high level of complexity may prevent a previously successful search strategy from locating the optimal solutions. Secondly, as the size of the solution space of the problem grows exponentially with the increasing number of decision variables, there is an urgent need to develop more effective and efficient search strategies to better explore this vast solution space with only limited computational budgets.

In recent years, researches on scaling up EAs to large scale problems have attracted much attention, including both theoretical and practical studies. Existing work on this topic are still rather limited, given the significance of the scalability issue. This special session is devoted to highlight the recent advances in EAs for handling large scale global optimization (LSGO) problems, involving single objective or multiple objectives, unconstrained or constrained, binary/discrete or real, or mixed decision variables. More specifically, we encourage interested researchers to submit their original and unpublished work on:

Furthermore, a companion competition on Large Scale Global Optimization (LSGO) will also be organized in conjunction with our special session. The competition allows participants to run their own algorithms on 20 benchmark functions, each of which is of 1000 dimensions. Detailed information about these benchmark functions is provided in the following technical report:

The aim of this competition is to provide a common platform that encourages fair and easy comparisons across different LSGO algorithms. Researchers are welcome to apply any kind of evolutionary computation technique to the test suite. The technique and the results can be reported in a paper for the special session (i.e., submitted via the online submission system of CEC'2015).

Please refer to the website below for the test suite package (implemented in Matlab, C++ and Java) and the lastest information about the competition:

For Python users, Prof. Molina is maintaining a Python version of the test suite, which can be found in the following website:

Paper Submission
Manuscripts should be prepared according to the standard format and page limit of regular papers specified in CEC'2015 and submitted through the CEC'2015 website: http://www.cec2015.org. Special session papers will be treated in the same way as regular papers and included in the conference proceedings.

Submission deadline: January 16, 2015

Special Session Organizers
Professor Ke Tang
The USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI)
School of Computer Science and Technology
University of Science and Technology of China, Hefei, Anhui, China
Email: ketang@ustc.edu.cn, Website: http://staff.ustc.edu.cn/~ketang

Associate Professor Xiaodong Li
School of Computer Science and Information Technology
RMIT University, Australia
Email: xiaodong.li@rmit.edu.au, Website: http://goanna.cs.rmit.edu.au/~xiaodong

Dr. Zhenyu Yang
College of Information System and Management
National University of Defense Technology (NUDT), Changsha, China
Email: zhyuyang@ieee.org

Associate Professor Daniel Molina
School of Engineering
University of Cadiz, Spain
Email: daniel.molina@uca.es