Decomposition Evolutionary Multi-Objective Optimization: What We Know from the Literature and What We are not Clear from a Data Science Perspective
Description
Evolutionary multi-objective optimization (EMO) has been a major research topic in the field of evolutionary computation for three decades. 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. Multi-objective evolutionary algorithm based on decomposition (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 evolutionary algorithms and traditional decomposition methods. It has been a commonly used evolutionary algorithmic framework in recent years. In this tutorial, we will provide a comprehensive literature review of MOEA/D and a graph data mining of the literature within this realm.
Organizers
Ke Li
Ke Li is an UKRI Future Leaders Fellow, a Turing Fellow, and a Reader in Computer Science at the University of Exeter. My research has contributed to the fundamental development of computational/artificial intelligence (CI/AI) for black-box optimisation and decision-making (especially with multiple conflicting objectives), as well as their applications in software engineering, renewable energy, and life sciences. I have published over 140 papers, nearly half of which are as the first/single author. These include 41 papers in prestigious IEEE/ACM Transactions and over 30 papers in top conferences across AI (e.g., NeurIPS, CVPR, AAAI, IJCAI), natural language processing (e.g., ACL, EMNLP), and software engineering (ICSE, FSE, ASE, ISSTA). Eight of my articles are in the ESI top 1% highly cited papers. One work has been ranked #1 in ‘Journal Impact Factor contributing items’ for IEEE Transactions on Evolutionary Computation (TEVC) by Clarivate Analytics in 2017. Two works have been nominated for the prestigious TEVC Outstanding Paper Award in 2016 and 2017. Since 2020, I have been recognised in the Stanford list of top 2% of the world most-cited scientists. I have secured nearly £4m grant funding from esteemed bodies, e.g., UKRI, Royal Society, EPSRC, ERC, EU Horizon, Hong Kong RGC, and NSFC (China).
Qingfu Zhang
Qingfu Zhang is a Professor at the Department of Computer Science, City University of Hong Kong. His main research interests include evolutionary computation, optimization, neural networks, data analysis, and their applications. He is currently leading the Metaheuristic Optimization Research (MOP) Group in City University of Hong Kong. Professor Zhang is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the IEEE Transactions Cybernetics. He was awarded the 2010 IEEE Transactions on Evolutionary Computation Outstanding Paper Award. He is on the list of the Thomson Reuters 2016 and 2017 highly cited researchers in computer science. He is an IEEE fellow.