Together with Prof.Hisao Ishibuchi, Dr. Ran Cheng, Dr. Miqing Li, we are organiz 2018-10-01 19:25:32
Special Session
Evolutionary Many-objective Optimization
 at 2019 IEEE Congress on Evolutionary Computation
June 10-13, 2019
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.
 
Paper Submission
 
Papers should be prepared according to the format and page limit of regular papers specified for CEC 2019. Paper submission should be done through the CEC 2019 website at the following link: http://www.cec2019.org/papers.html#submission
Papers submitted to the special session will be treated in the same way as regular papers and will be included in the conference proceedings.
 
-           Paper submission: 7 January, 2019
-           Decision notification: 7 March, 2019
-           Camera ready paper due: 31 March, 2019
-           Registration: 31 March, 2019
-           Conference: 10 June, 2019
 
Note: recent evolutionary many-objective optimization algorithms (not limited to)
·         R. C. Purshouse and P. J. Fleming, “On the evolutionary optimization of many conflicting objectives,” IEEE Trans. Evol. Computat., vol. 11, no. 6, pp. 770–784, Dec. 2007.
·         R. Wang, R. C. Purshouse, and P. J. Fleming, “Preference-inspired coevolutionary algorithms for many-objective optimization,” Evolutionary Computation, IEEE Transactions on, vol. 17, no. 4, pp. 474–494, 2013.
·         R. Wang, Z. B. Zhou, H. Ishibuchi, T.J. Liao, T. Zhang, “Localized weighted sum method for many-objective optimization,” IEEE Transactions on Evolutionary Computation,  early access, 2016
·         B. Li, J. Li, K. Tang, and X. Yao, “Many-objective evolutionary algorithms: A survey,” Acm Computing Surveys, vol. 48, no. 1, pp. 1–35, 2015.
·         J. Bader and E. Zitzler, “HypE: An algorithm for fast hypervolume based many-objective optimization,” Evol. Computat., vol. 19, no. 1, pp. 45–76, Jan. 2011.
·         S. Yang, M. Li, and J. Zheng, “A grid-based evolutionary algorithm for many-objective optimization,” IEEE Trans. Evol. Comput., vol. 17, no. 5, pp. 721-736.
·         M. Li, S. Yang, and X. Liu, “Shift-based density estimation for Paretobased algorithms in many-objective optimization,” IEEE Transactions on Evolutionary Computation, vol. 18, no. 3, pp. 348-365, 2014.
·         M. Li, S. Yang, and X. Liu. Diversity comparison of Pareto front approximations in many-objective optimization. IEEE Transactions on Cybernetics, 44(12): 2568-2584, December 2014.
·         K. Deb and H. Jain, “An evolutionary many-objective optimization algorithm using reference-point based non-dominated sorting approach, Part I: solving problem with box constraints,” IEEE Trans. Evolut. Comput., vol. 18, no. 4, pp. 577-601.
·         H. Wang, L. Jiao, and X. Yao, “Two_Arch2: an improved two-archive algorithm for many-objective optimization,” IEEE Transactions on Evolutionary Computation, vol. 19, no. 4, pp. 524-541, 2015.
·         M. Li, S. Yang, and X. Liu. Bi-goal evolution for many-objective optimization problems. Artificial Intelligence, 228: 45-65, November, 2015.
·         Y. Yuan, H. Xu, B. Wang, and X. Yao, “A new dominance relation based evolutionary algorithm for many-objective optimization,” IEEE Transactions on Evolutionary Computation, vol. 20, no. 1, pp. 16–37, 2016.
·         R. Cheng, Y. Jin, M. Olhofer, and B. Sendhoff, “A reference vector guided evolutionary algorithm for many-objective optimization,” IEEE Transactions on Evolutionary Computation,  early access, 2016
·         X. Zhang, Y. Tian, and Y. Jin, “A knee point driven evolutionary algorithm for many-objective optimization,” IEEE Transactions on Evolutionary Computation, early access, 2016.
·         Z. He and G. G. Yen, “Visualization and performance metric in manyobjective optimization,” IEEE Transactions on Evolutionary Computation, early access, 2016
·         S. Jiang and S. Yang. A strength pareto evolutionary algorithm based on reference direction for multi-objective and many-objective optimization. IEEE Transactions on Evolutionary Computation, early access, 2016. 
Organizers
 
-         Rui Wang, Associate Professor (Email: ruiwangnudt@gmail.com)
College of System Engineering, National University of Defense Technology, P.R.China.
 
-         Ran Cheng, Research Fellow (Email: ranchengcn@gmail.com)
School of Computer Science, University of Birmingham, United Kingdom.
 
-         Miqing Li, Research Fellow (Email: limitsing@gmail.com)
School of Computer Science, University of Birmingham, United Kingdom.
 
-         Hisao Ishibuchi, Professor (Email: hisaoi@cs.osakafu-u.ac.jp)
Department of Computer Science and Engineering, Southern University of Science and Technology, China
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