Hybrid evolutionary multi-criteria decision-making methods
at The IEEE SSCI 2016 MCDM Symposium
Organizers: Rui Wang (email@example.com), Tao Zhang, Yang Shengxiang, Jian Xiong
Complex problems usually require the simultaneous consideration of multiple performance criteria within multidisciplinary environments. Since the middle of the 1990s, the field of Evolutionary Multi-Criterion Optimization (EMO) has used a population-based heuristic approach for addressing such problems. This is evidenced by the rapidly growing number of research publications and by the availability of a number of related software tools and users (academia and industry). For some time now, EMO researchers have understood the necessity to develop and integrate decision making aspects into EMO approaches, and so the need for cross-fertilization between EMO and the Multiple Criteria Decision Making (MCDM) and Multiple Criteria Decision Aid (MCDA) communities has become apparent. The aim of this special session is to continue the integration and blending of ideas between EMO, MCDM and MCDA researchers, and to stimulate engagement with the user community.
Full papers are invited for a special session on new horizons for multi-criteria decision-making, which may be pathfinders for a step-change in multidisciplinary decision-making, or consider hybrid EMO-MCDM/MCDA methods and applications, or showcase developments in the MCDM/MCDA communities that have potential for blending with EMO themes.
The topics include, but are not limited to:
Interactive Multi-objective Optimization
Hybrid EMO-MCDM methodologies.
Multiple Criteria Choice, Ranking, and Sorting
Multiple Objective Continuous and Combinatorial Optimization
Evolutionary Many-objective Optimization
Multiple Attribute Utility Theory
Multiple Criteria Decision Aiding
Multiple Objective Metaheuristics
Fuzzy Multiple Criteria Decision Making
Real-world applications of EMO, MCDA in government, business, industry and interdisciplinary sciences.
Paper submission: July 18, 2016
Paper acceptance: September 12, 2016
Final submission: October 10, 2016
Early registration: October 10, 2016
Please refer to the following papers for a brief introduction to Hybrid evolutionary multi-criteria decision-making methods
 C. Coello, ¡°Handling preferences in evolutionary multiobjective optimization: A survey,¡± in Evolutionary Computation (CEC), 2000 IEEE Congress on, vol. 1. IEEE, 2000, pp. 30¨C37.
 L. Rachmawati and D. Srinivasan, ¡°Preference Incorporation in Multiobjective Evolutionary Algorithms: A Survey,¡± in Evolutionary Computation (CEC), 2006 IEEE Congress on. IEEE, 2006, pp. 962¨C968.
 (PDF) Purshouse, R. C., Deb, K., Mansor, M. M., Mostaghim, S., Wang, R., A Review of Hybrid Evolutionary Multiple Criteria Decision Making Methods , In Evolutionary Computation (CEC), 2014 IEEE Congress on (pp. 1147-1154). IEEE, Beijing, China.
Dr. Rui Wang, Prof. Tao Zhang, Dr. Jian Xiong
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