Tutorial: Decomposition Multi-Objective Optimization: Current Developments and Future Opportunities

Evolutionary multi-objective optimization (EMO) has been a major re- search topic in the field of evolutionary computation for many years. It has been generally accepted that combination of evolutionary algorithms and traditional optimization methods should be a next generation multi-objective optimization solver. As the name suggests, the basic idea of the decomposition-based technique is to transform the original complex problem into simplified subproblem(s) so as to facilitate the optimization. Decomposition methods have been well used and studied in traditional multi-objective optimization. MOEA/D decomposes a multi-objective problem into a number of subtasks, and then solves them in a collaborative manner. MOEA/D provides a very natural bridge between multi-objective evo- lutionary algorithms and traditional decomposition methods. It has been a commonly used evolutionary algorithmic framework in recent years.

An outline of the tutorial: Within this tutorial, a comprehensive introduction to MOEA/D will be given and selected research results will be presented in more detail. More specifically, we are going to (i) in- troduce the basic principles of MOEA/D in comparison with other two state-of-the-art EMO frameworks, i.e., Pareto- and indicator-based frameworks; (ii) present a general overview of state-of-the-art MOEA/D variants and their applications; (iii) discuss the future opportunities for possible further developments.

Learning outcomes: The intended audience of this tutorial can be both novices and people familiar with EMO or MOEA/D. In particular, it is self-contained that foundations of multi-objective optimization and the basic working principles of EMO algorithms will be included for those without experience in EMO to learn. Open questions will be posed and highlighted for discussion at the latter session of this tutorial.

Expected length of the tutorial: This tutorial is expected to be less than 2 hours including the discussion session.

The level of the tutorial: This tutorial is advanced level but with an introduction at the outset.


Ke Li is a Senior Lecturer (Associate Professor) in Computer Science at the Department of Computer Science, University of Exeter. He earned his PhD from City University of Hong Kong. Afterwards, he spent a year as a postdoctoral research associate at Michigan State University. Then, he moved to the UK and took the post of research fellow at University of Birmingham. His current research interests include the evolutionary multi-objective optimization, automatic problem solving, machine learning and applica- tions in water engineering and software engineering. He is the founding chair of IEEE CIS Task Force on Decomposition-based Techniques in Evolutionary Computation. He currently serves as an associate editor of IEEE Transactions on Evolutionary Computation, International Journal of Machine Learning and Cyber- netics and Complex & Intelligent Systems. He served as a guest editor in Neurocomputing Journal and Multimedia Tools and Applications Journal. His current research interests include the evolutionary multi- objective optimization, automatic problem solving, machine learning and applications in water engineering and software engineering. Recently, he has been awarded a prestigious UKRI Future Leaders Fellowship.