Multi-objective Optimization Evolutionary Algorithms (MOEA/D) have been a widely used and studied Evolutionary Multi-objective Optimization (EMO) algorithmic framework over the last few years. MOEA/D borrows ideas from traditional optimization. It decomposes a multi-objective problem into a number of sub-tasks, and then solves them in a collaborative manner. MOEA/D provides a very natural bridge between multi-objective evolutionary algorithms and traditional decomposition methods. In this talk, Professor Zhang will explain the basic ideas behind MOEA/D and some recent developments. He will also outline some possible research issues in multi-objective evolutionary computation.
This event was hosted by the NAFEMS Optimisation Working Group (OWG). The OWG has formed an online Community to help disseminate best practice and encourage the adoption of optimisation methods and technology. You can discuss this and other presentations on the Optimisation Community Forum. For more information and to get involved go to the Optimisation Community webpage.
|Date||5th December 2017|
|Organisation||City University of Hong Kong|