Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial Optimization

Robbert Reijnen, Yaoxin Wu, Zaharah Bukhsh, Yingqian Zhang

Keywords: Deep Reinforcement Learning, Multi-objective Combinatorial Optimization, Dynamic Algorithm Configuration, Evolutionary algorithm

TL;DR: This work proposes a new approach using deep reinforcement learning (DRL) and graph neural networks (GNNs) to dynamically configure multi-objective evolutionary algorithms for solving multi-objective combinatorial optimization problems.

Abstract: Deep reinforcement learning (DRL) has been widely used for dynamic algorithm configuration, especially for evolutionary algorithms, which benefit from adaptive update of parameters during the algorithmic execution. However, applying DRL to algorithm configuration for multi-objective combinatorial optimization (MOCO) problems remains relatively unexplored. This paper presents a novel graph neural network (GNN) based DRL to configure multi-objective evolutionary algorithms. We model the dynamic algorithm configuration as a Markov decision process, representing the convergence of solutions in the objective space by a graph, with their embeddings learned by a GNN to enhance the state representation. Experiments on diverse MOCO challenges indicate that our method outperforms traditional and DRL-based algorithm configuration methods in terms of efficacy and adaptability. It also exhibits advantageous generalizability across objective types and problem sizes, and prospective applicability to different evolutionary algorithms.

Primary Area: optimization

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Submission Number: 6550