Multi-objective optimization in dynamic environment: A review

Dynamic multi-objective evolutionary algorithms (Dynamic MOEAs) use the evolutionary algorithms to solve the dynamic multi-objective optimization problems (DMOPs). It has become one of the hot areas of research. The challenge of DMOPs is that the objective functions, the constraints or the parameters may change over time. This paper tries to provide a comprehensive overview of the related work, which is organized by the common process of Dynamic MOEAs, such as, the detection of change, the maintenance of diversity, the prediction of change, the test problems and the performance metrics. Finally, topics for further research are suggested.