Efficient Meta Neural Heuristic for Multi-Objective Combinatorial
  Optimization
Jinbiao Chen Jiahai Wang Zizhen Zhang Zhiguang Cao Te Ye Siyuan Chen
Recently, neural heuristics based on deep reinforcement learning have
exhibited promise in solving multi-objective combinatorial optimization
problems (MOCOPs). However, they are still struggling to achieve high learning
efficiency and solution quality. To tackle this issue, we propose an efficient
meta neural heuristic (EMNH), in which a meta-model is first trained and then
fine-tuned with a few steps to solve corresponding single-objective
subproblems. Specifically, for the training process, a (partial)
architecture-shared multi-task model is leveraged to achieve parallel learning
for the meta-model, so as to speed up the training; meanwhile, a scaled
symmetric sampling method with respect to the weight vectors is designed to
stabilize the training. For the fine-tuning process, an efficient hierarchical
method is proposed to systematically tackle all the subproblems. Experimental
results on the multi-objective traveling salesman problem (MOTSP),
multi-objective capacitated vehicle routing problem (MOCVRP), and
multi-objective knapsack problem (MOKP) show that, EMNH is able to outperform
the state-of-the-art neural heuristics in terms of solution quality and
learning efficiency, and yield competitive solutions to the strong traditional
heuristics while consuming much shorter time.