A Hybrid Metaheuristic Framework with Reinforcement Learning–Based Heuristic Selection for Large-Scale Combinatorial Optimization

Abstract

This paper proposes a novel Hybrid Metaheuristic Framework (HMF) that integrates multiple optimization strategies within a self-adaptive mechanism for solving large-scale combinatorial problems. Unlike traditional metaheuristics, which rely on fixed algorithmic parameters and predefined strategies, HMF dynamically selects and adjusts heuristic operators using a reinforcement learning-based adaptive mechanism.  Specifically, the framework employs Deep Q-Learning to guide real-time parameter tuning and metaheuristic selection based on performance feedback from the optimization process. The study applies this framework to three benchmark problems: the Vehicle Routing Problem (VRP), the Facility Location Problem (FLP), and the Job Scheduling Problem (JSP), demonstrating consistent gains in solution quality and convergence speed. Experimental results reveal that HMF achieves noticeable improvements over conventional methods in both runtime efficiency and optimization effectiveness, without requiring manual parameter tuning. The findings establish the proposed framework as a promising tool for real-world large scale combinatorial optimization challenges.