  One of the major distinguishing features of the dynamic multiobjective
optimization problems (DMOPs) is the optimization objectives will change over
time, thus tracking the varying Pareto-optimal front becomes a challenge. One
of the promising solutions is reusing the "experiences" to construct a
prediction model via statistical machine learning approaches. However most of
the existing methods ignore the non-independent and identically distributed
nature of data used to construct the prediction model. In this paper, we
propose an algorithmic framework, called Tr-DMOEA, which integrates transfer
learning and population-based evolutionary algorithm for solving the DMOPs.
This approach takes the transfer learning method as a tool to help reuse the
past experience for speeding up the evolutionary process, and at the same time,
any population based multiobjective algorithms can benefit from this
integration without any extensive modifications. To verify this, we incorporate
the proposed approach into the development of three well-known algorithms,
nondominated sorting genetic algorithm II (NSGA-II), multiobjective particle
swarm optimization (MOPSO), and the regularity model-based multiobjective
estimation of distribution algorithm (RM-MEDA), and then employ twelve
benchmark functions to test these algorithms as well as compare with some
chosen state-of-the-art designs. The experimental results confirm the
effectiveness of the proposed method through exploiting machine learning
technology.
