Evolutionary Many-objective Optimization
at 2017 IEEE Congress
on Evolutionary Computation
Donostia - San Sebasti芍n, Spain
June 5-8, 2017
Organizers: Rui Wang, Shengxiang Yang, Sanaz Mostaghim and Tao Zhang
Multi-objective optimization problems arise regularly in real-world where multiple objectives are required to be optimized at the same time. Evolutionary multi-objective algorithms are well suited to solve multi-objective optimization problems since their population based nature can generate a set of trade-off solutions in a single run. So far, MOEAs have been demonstrated as effective in addressing MOPs with two and three objectives. However, they tend to face difficulties on addressing MOPs with four or more objectives, the so called many-objective problems.
The difficulties include, for example, the deterioration of convergence, the large number of solutions required to approximate the entire Pareto front, the solutions visualization, performance metrics. This special session focuses on evolutionary many-objective optimization to tackle problems in many-objective optimization including the above mentioned difficulties.
Full papers are invited on recent advances, new horizons in evolutionary many-objective optimization. Also, we are interested in various studies discussing issues related to many-objective optimization, particularly, the real-world problems. You are invited to submit papers that are unpublished original work for this special session. The topics include, but are not limited to:
Preference based search
Dimensionality reduction of the objective space
Visualization of high dimensional space and decision making
Search performance metrics
Hybrid evolutionary many-objective algorithms
Many-objective real-world optimization problems
Papers should be prepared according to the format and page limit of regular papers specified for CEC 2017. Paper submission should be done through the CEC 2017 website at the following link: http://www.cec2017.org/?utm_source=researchbib
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 deadline: January 16, 2017
Notification of acceptance: February 26, 2017
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每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每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每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每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每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.
Dr. Rui Wang, Prof. Tao Zhang
College of Information Systems and Management,
National University of Defense Technology,
Changsha, Hunan, P.R.China
Prof. Shengxiang Yang
School of Computer Science and Informatics, De Montfort University, United Kingdom
Prof. Sanaz Mostaghim
Faculty of Computer Science, University of Magdeburg, Germany