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

TL;DR

This paper introduces a hybrid approach combining evolutionary algorithms and deep reinforcement learning to effectively solve multi-objective orienteering problems by decomposing them into subproblems and iteratively optimizing solutions.

Contribution

The study presents a novel hybrid framework integrating MOEA and DRL for MO-OPs, demonstrating superior performance and generalization over existing methods.

Findings

Outperforms NSGA-II and NSGA-III on various test instances.

Exhibits strong generalization ability across different problem types.

Effectively decomposes MO-OP into subproblems for targeted optimization.

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.