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 sub-problems for which solutions for the original global problem are computed and aggregated cooperatively. This simple idea, which is relatively 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 concerning the objective space, typically employing scalarizing functions like in the MOEA/D framework. We, however, encourage contributions reporting advances concerning other decomposition techniques operating in the decision space as done in the so-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 considerable body of literature dedicated to other well-established approaches (e.g., Pareto ranking, indicator-based techniques, etc.), and relatively few research forums have been devoted 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, intending to understand their key ingredients and their central dynamics, as well as 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 an excellent opportunity for researchers in the evolutionary and multi-objective optimization field to exchange their recent ideas and advances on the design and analysis of DEMO approaches. In this respect, we welcome 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 eliciting the main design principles that lead to effective and efficient cooperative search procedures among the so-defined single or multiple objective subproblems.

Important Dates:

Paper Submission Deadline January 15th, 2024 January 29th, 2024 (Extended Deadline)

Paper Acceptance Notification March 15th, 2024

Final Paper Submission & May 1st, 2024
Early Registration Deadline

The submission procedure, deadlines, and paper format are identical to those for the WCCI'2024 main conference. In particular, papers must be submitted through the WCCI'2024 online submission system, and the ADEMO special session must be selected from the list of research topics.