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.