ObjectivesIn 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), Artificial Bee Colony (ABC), Estimation of Distribution Algorithms (EDA), etc. Although these techniques have shown excellent search capabilities when applied to small or medium sized problems, they still encounter serious challenges when applied 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), which 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 a limited computational budget.
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 however 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 or multiple objectives problems, unconstrained or constrained search spaces and binary/discrete, 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 More specifically, we encourage interested researchers to submit their original and unpublished work on: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.
- Specialized algorithms for large scale optimization.
- Novel test suites that help us to understand large scale optimization problem characteristics.
- New grouping approaches for cooperative coevolution.
There is not proposed a particular competition, however, for people interested in LSGO for real-coding, we recommend the following benchmark using a dimensionality of 1000 variables:
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.
Paper SubmissionManuscripts should be prepared according to the standard format and page limits of regular papers specified in CEC’2016 and submitted through the WCCI’2016 website: http://www.wcci2016.org.
Special Session OrganizersAssociate Professor Daniel Molina
School of Engineering, Computer Science
University of Cadiz, Cádiz, Spain
Dr. Swagatam Das
Electronics and Communication Sciences Unit
Indian Statistical Institute, Kolkata, India
Dr. Antonio LaTorre
School of Computer Engineering
Technical University of Madrid, Madrid, Spain