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Multi-objective orienteering problems (MO-OPs) are classical multi-objectiverouting problems and have received a lot of attention in the past decades. Thisstudy seeks to solve MO-OPs through a problem-decomposition framework, that is,a MO-OP is decomposed into a multi-objective knapsack problem (MOKP) and atravelling salesman problem (TSP). The MOKP and TSP are then solved by amulti-objective evolutionary algorithm (MOEA) and a deep reinforcement learning(DRL) method, respectively. While the MOEA module is for selecting cities, theDRL module is for planning a Hamiltonian path for these cities. An iterativeuse of these two modules drives the population towards the Pareto front ofMO-OPs. The effectiveness of the proposed method is compared against NSGA-IIand NSGA-III on various types of MO-OP instances. Experimental results showthat our method exhibits the best performance on almost all the test instances,and has shown strong generalization ability.

Hybridization of evolutionary algorithm and deep reinforcement learning for multi-objective orienteering optimization