  Deep reinforcement learning (DRL) has recently shown its success in tackling
complex combinatorial optimization problems. When these problems are extended
to multiobjective ones, it becomes difficult for the existing DRL approaches to
flexibly and efficiently deal with multiple subproblems determined by weight
decomposition of objectives. This paper proposes a concise meta-learning-based
DRL approach. It first trains a meta-model by meta-learning. The meta-model is
fine-tuned with a few update steps to derive submodels for the corresponding
subproblems. The Pareto front is then built accordingly. Compared with other
learning-based methods, our method can greatly shorten the training time of
multiple submodels. Due to the rapid and excellent adaptability of the
meta-model, more submodels can be derived so as to increase the quality and
diversity of the found solutions. The computational experiments on
multiobjective traveling salesman problems and multiobjective vehicle routing
problem with time windows demonstrate the superiority of our method over most
of learning-based and iteration-based approaches.
