6th Special Session on Advances in Decomposition-based Evolutionary Multi-objective Optimization (ADEMO)

Past EditionsADEMO 2016 (Vancouver, Canada) ADEMO 2018 (Rio de Janeiro, Brazil) ADEMO 2019 (Wellington, New Zealand) ADEMO 2020 (Glaswo, UK) ADEMO 2021 (Kraków, Poland)

18-23 July, 2022.

Centro Congressi, Padova, Italy.


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 sub-problems for which solutions for the original global problem are computed and aggregated in a cooperative manner. This simple idea, which is rather standard in computer science and information systems, allows us to open up new exciting research perspectives and challenges both at the fundamental level of our understanding of multi-objective problems and in terms of designing and implementing new efficient algorithms for solving them. Generally speaking, the special session will focus on stochastic evolutionary approaches for which decomposition is performed with respect to the objective space, typically by means of scalarizing functions like in the MOEA/D framework. We, however, encourage contributions reporting advances with respect to other decomposition techniques operating in the decision space as done in the co-called cone-separation methods; or other hybrid approaches taking inspiration from operations research and mathematical programming. In fact, many different DMOEAs variants have been proposed, studied and applied to various application domains in recent years. However, DMOEAs 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), and relatively few research forums have been dedicated to the study of DEMO approaches and their unification. 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, tuning, and performance assessment of DMOEAs.

The special session will be a nice opportunity for researchers in the evolutionary and multiobjective optimization field to exchange their recent ideas and advances on the design and analysis of DEMO approaches. In this respect, we are welcoming high-quality papers in theoretical, developmental, implementational, and applied aspects of DEMO approaches. More particularly, the special session will encourage original research contributions that address new and existing DMOEAs, their contributions and relationships to other methodologies dedicated to multi-objective optimization in terms of algorithmic components, decomposition strategies, collaboration among different search procedures, design of new specialized search procedures, parallel and distributed implementations, incorporation of user interaction, combination and hybridization with other traditional (heuristic or exact) techniques, strategies for dealing with many objectives, noisy problems, and expensive problems, problem solving and applications, etc. The main focus will be on eliciting the main design principles that lead to effective and efficient cooperative search procedures among the so-defined single or multiple objective subproblems.

Topics of interests

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

  1. Analysis of algorithmic components and performance assessment of DEMO approaches

  2. Experimental and theoretical investigations on the accuracy of the underlying decomposition strategies, e.g. scalarizing functions techniques, multiple reference points, variable grouping, etc.

  3. Adaptive, self-adaptive, and tuning aspects for the parameter setting and configuration of DEMO approaches

  4. Design and analysis of new DEMO approaches dedicated to specific combinatorial, constrained and/or continuous domains

  5. 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 (CMA-ES), Scatter Search (SS), etc.

  6. Adaptation and analysis of DEMO approaches in the context of large scale and many-objective problem solving

  7. Application of DEMO for real-world problem solving

  8. Design and implementation of DEMO approaches in massively parallel and large scale distributed environments (e.g., GPUs, Clusters, Grids, etc)

  9. Software tools for the design implementation and performance assessment of DEMO approaches

Important Dates:

Paper submission: January 31, 2022 (11:59 PM AoE) STRICT DEADLINE

Notification of acceptance: April 26, 2022

Final paper submission: May 23, 2022

Submission procedure, deadlines, and paper format are the same as the IEEE WCCI'2022 main conference. In particular, we recall that papers must be submitted through the IEEE WCCI 2022 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, UAM-Cuajimalpla, México (contact: saul.zapotecas [at] gmail.com).

  • Bilel Derbel, University of Lille, Inria, France

  • Ke Li, University of Exeter, UK

  • Qingfu Zhang, City University, Hong Kong