Call for Papers
2018 IEEE World Congress on Computational Intelligence (IEEE WCCI 2018)


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Special Session: Advances in Decomposition-­based Evolutionary Multi-­objective Optimization

2nd Special Session on Evolutionary Multi-objective Optimization based on Decomposition @ IEEE-WCCI/CEC 2018 8-13 July, Rio de Janeiro, Brazil.
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*** Scope

The purpose of this special session is to promote the design, study, and validation of generic approaches for solving multi­-objective optimization problems based on the concept of decomposition. Decomposition-based Evolutionary Multi-­objective Optimization (DEMO) encompasses any technique, concept or framework that takes inspiration from the "divide and conquer" paradigm, by essentially breaking a multi-­objective optimization problem into several sub­problems for which solutions for the original global problem are computed and aggregated in a cooperative manner.
We encourage contributions reporting advances with respect to other decomposition techniques operating in the decision space or other hybrid approaches taking inspiration from operations research and mathematical programming. Many different DMOEAs variants have been proposed, studied and applied to various application domains. However, DEMOs are still in their very early infancy, since only a few basic design principles have been established compared to the huge body of literature dedicated to other well-established approaches (e.g. Pareto ranking, indicator-based techniques, etc). The main goal of the proposed session is to encourage research studies that systematically investigate the critical issues in DMOEAs at the aim of understanding their key ingredients and their main dynamics, as well a to develop solid and generic principles for designing them. The long-term goal is to contribute to the emergence of a general and unified methodology for the design, the tuning and the performance assessment of DEMOs.

*** Topics of interests

The topics of interests include (but are not limited to) the following issues:

1. Analysis of algorithmic components and performance assessment of DEMO approaches
Experimental and theoretical investigations on the accuracy of the underlying decomposition strategies, e.g. scalarizing functions techniques, multiple reference points, variable grouping, etc.
2.Adaptive, self­adaptive, and tuning aspects for the parameter setting and configuration of DEMO approaches.
3. Design and analysis of new DEMO approaches dedicated to specific combinatorial, constrained and/or continuous domains.
4. Effective hybridization of single-objective solvers with DEMO approaches, i.e., plug and­ play algorithms based on traditional single objective evolutionary algorithms and meta­ heuristics, such as: Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO), Covariance Matrix Evolution Strategy (CMA­ES), Scatter Search (SS), etc.
5. Adaptation and analysis of DEMO approaches in the context of large scale and many objective problem solving
6. Application of DEMO for solving real-­world problems.
7. Design and implementation of DEMO approaches in massively parallel and large scale distributed environment (e.g., GPUs, Clusters, Grids, etc).
8. Software tools for the design implementation and performance assessment of DEMO approaches


*** Deadlines and Submission

Submission Deadline: Jan 15, 2018
Notification Due: Mar 15, 2018
Final Version Due: May 1, 2018

Submission procedure, deadlines, and paper format are same than the IEEE-WCCI/CEC'18 main conference. In particular, we recall that papers must be submitted through the IEEE WCCI 2018 online submission system while selecting the ADEMO special session under the list of research topics in the submission system.


*** Organizers and Contact

--Saúl Zapotecas­-Martínez (saul.zapotecas [at] gmail.com)
Universidad Autónoma Metropolitana (UAM), Cuajimalpla, México

--Bilel Derbel (bilel.derbel [at] univ­lille1.fr)
University Lille 1, CRIStAL CNRS UMR9189, France
DOLPHIN, Inria Lille Nord Europe, France

--Qingfu Zhang (qingfu.zhang [at] cityu.edu.hk)
City University of Hong Kong, Hong Kong