Evolutionary Many-objective Optimization：methods and application
Motivations and Theme
The field of evolutionary multi-objective optimization has developed rapidly over the last 20 years, but the design of effective algorithms for addressing problems with more than three objectives (called many-objective optimization problems, MaOPs) remains a great challenge. First, the ineffectiveness of the Pareto dominance relation, which is the most important criterion in multi-objective optimization, results in the underperformance of traditional Pareto-based algorithms. Also, the aggravation of the conflict between convergence and diversity, along with increasing time or space requirement as well as parameter sensitivity, has become key barriers to the design of effective many-objective optimization algorithms. Furthermore, the infeasibility of solutions' direct observation can lead to serious difficulties in algorithms' performance investigation and comparison. All of these suggest the pressing need of new methodologies designed for dealing with MaOPs, new performance metrics and test functions tailored for experimental and comparative studies of evolutionary many-objective optimization (EMaO) algorithms.
List of Topics
We welcome high-quality original submissions addressing various topics related to evolutionary many-objective optimization, but are not limited to:
- Algorithms for evolutionary many-objective optimization, including search operators, mating selection, environmental selection and population initialization;
- Performance indicators for evolutionary many-objective optimization;
- Benchmark functions for evolutionary many-objective optimization;
- Visualization techniques for evolutionary many-objective optimization;
- Objective reduction techniques for evolutionary many-objective optimization;
- Preference articulation and decision making methods for evolutionary many-objective optimization;
- Constraint handling methods for evolutionary many-objective optimization;
- Evolutionary many-objective optimization in combinatorial/discrete problems;
- Evolutionary many-objective optimization in dynamic environments;
- Evolutionary many-objective optimization in large-scale problems；
- Artificial intelligence methods developed for many-objective optimization.
Papers should be prepared according to the format and page limit of regular papers specified for
CEC 2021. Paper submission should be done through the CEC 2021 website at the following link:
Papers submitted to the special session will be treated in the same way as regular papers and will be included in the conference proceedings.
Please feel free to contact Rui Wang via Email: email@example.com
Short Biography of the Organizers
Rui Wang received the Ph.D. degree in system engineering from The University of Sheffield, U.K. in 2014. He is currently an Associate Professor at the College of Systems Engineering, National University of Defense Technology, P.R. China. His main research interests are evolutionary multi- objective optimization, computational intelligence methods on energy internet. Dr. Wang is the co-organizer of a number of special sessions with respect to evolutionary multi-objective optimization at IEEE WCCI, SSCI conferences. He is the recipients of The Operational Research Society Ph.D. Prize Runners-Up for the Best Ph.D. Dissertation in 2014, of the Funds for Distinguished Young Scientists from the Natural Science Foundation of Hunan province in 2016, of the Wu Wen-Jun Artificial Intelligence Outstanding Young Scholar.
Miqing Li received the Ph.D. degree in computer science from Brunel University London, U.K. in 2015. He is currently a research fellow with CERCIA, School of Computer Science, University of Birmingham, U.K. His research interests are evolutionary multi- and many-objective optimization and its diverse applications. Dr. Li has published over 30 journal and conference papers.