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 More specifically, we encourage interested researchers to submit their original and unpublished work on:
- Theoretical and experimental analysis of the scalability of EAs;
- Novel approaches and algorithms for scaling up EAs to large scale optimization problems;
- Applications of EAs to real-world large scale optimization problems;
- Novel test suites that help us understand large scale optimization problem characteristics.
X. Li, K. Tang, M. Omidvar, Z. Yang and K. Qin, "Benchmark Functions for the CEC'2013 Special Session and Competition on Large Scale Global Optimization," Technical Report, Evolutionary Computation and Machine Learning Group, RMIT University, Australia, 2013
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).
Paper SubmissionManuscripts 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.
Special Session OrganizersProfessor 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: firstname.lastname@example.org, Website: http://staff.ustc.edu.cn/~ketang
Associate Professor Xiaodong Li
School of Computer Science and Information Technology,
RMIT University, Australia
Email: email@example.com, 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
Associate Professor Daniel Molina
School of Engineering
University of Cádiz, Spain
Organizer Bios:Ke Tang received the B.Eng. degree from the Huazhong University of Science and Technology, Wuhan, China, and the Ph.D. degree from the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, respectively. He is now a professor at 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, China. He has authored or co-authored more than 70 refereed papers in journals and conferences. His research interests include evolutionary computation, machine learning, and real-world applications. He is an Associate Editor of the IEEE Computational Intelligence Magazine and Computational Optimization and Applications. He served as the Program/Technical Co-Chair of CEC2010 and CEC2013.
Xiaodong Li received his B.Sc. degree from Xidian University, Xi'an, China, and Ph.D. degree in information science from University of Otago, Dunedin, New Zealand, respectively. Currently, he is an Associate Professor at the School of Computer Science and Information Technology, RMIT University, Melbourne, Australia. His research interests include evolutionary computation, machine learning, complex systems, multiobjective optimization, and swarm intelligence. He serves as an Associate Editor of the IEEE Transactions on Evolutionary Computation and International Journal of Swarm Intelligence Research. He is a founding member and currently a Vice-chair of IEEE CIS Task Force on Swarm Intelligence, and currently a Chair of IEEE CIS Task Force on Large Scale Global Optimization. He was the General Chair of SEAL'08, a Program Co-Chair AI'09, and a Program Co-Chair for IEEE CEC’2012. He is the recipient of 2013 SIGEVO Impact Award.
Zhenyu Yang received the B.Eng. and Ph.D. degrees from the University of Science and Technology of China, Hefei, China, in 2005 and 2010, both in computer science. He is currently a Lecturer at the College of Information Systems and Management, National University of Defense Technology, Changsha, China. His research interests include metaheuristics, such as evolutionary algorithms for global optimization, large-scale optimization, and various real-world applications. He is a founding member of the IEEE CIS Task Force on Large Scale Global Optimization. He serves as an Associate Editor of Computational Optimization and Applications.
Daniel Molina received the B.Eng. Degree from the University of Granada, Spain, and the Ph.D. from the same University at 2007. He is currently a Lecturer at the School of Engineering, University of Cadiz. He has authored or co-authored more than 40 refereed papers in journals and conferences. His research interests include metaheuristics, such as evolutionary algorithms for global optimization, niching optimization, large-scale optimization, and real-world applications. He is a member of the IEEE CIS Task Force on Large Scale Global Optimization.