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

• URL: http://arxiv.org/abs/2206.10464v1
• Date: Tue, 21 Jun 2022 15:20:42 GMT
• Title: Hybridization of evolutionary algorithm and deep reinforcement learning for multi-objective orienteering optimization
• Authors: Wei Liu, Rui Wang, Tao Zhang, Kaiwen Li, Wenhua Li and Hisao Ishibuchi
• Abstract summary: Multi-objective orienteering problems (MO-OPs) are classical multi-objective routing problems. This study seeks to solve MO-OPs through a problem-decomposition framework.
• Score: 16.23652137705642
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-objective orienteering problems (MO-OPs) are classical multi-objective routing problems and have received a lot of attention in the past decades. This study 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 a travelling salesman problem (TSP). The MOKP and TSP are then solved by a multi-objective evolutionary algorithm (MOEA) and a deep reinforcement learning (DRL) method, respectively. While the MOEA module is for selecting cities, the DRL module is for planning a Hamiltonian path for these cities. An iterative use of these two modules drives the population towards the Pareto front of MO-OPs. The effectiveness of the proposed method is compared against NSGA-II and NSGA-III on various types of MO-OP instances. Experimental results show that our method exhibits the best performance on almost all the test instances, and has shown strong generalization ability.

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