Advances in Production Engineering & Management
Volume 20 | Number 3 | September 2025 | pp 391–414
https://doi.org/10.14743/apem2025.3.548
Dynamic Harris Hawks Optimization and deep reinforcement learning framework for autonomous vehicle path planning
Zou, Q.; Yuan, X.; Liu, F.; Yin, Y.; Chen, P.
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A B S T R A C T
Urban intelligent transportation systems require real‑time, near‑optimal routing for autonomous vehicles navigating dynamic and uncertain traffic. We propose a Harris Hawks Optimization–deep reinforcement learning framework (HHO‑DRL) that unites HHO’s global exploration with DRL’s adaptive policy search through (i) a dynamic‑weight fusion scheme that continuously balances exploration and exploitation and (ii) a bidirectional experience‑feedback loop that exchanges elite solutions between the two solvers. On 23 CEC‑2014 benchmark functions and five classical multimodal tests, HHO‑DRL lowers mean error by up to three orders of magnitude relative to PSO and adaptive HHO, demonstrating superior robustness and precision. In 30 × 30 grid‑world simulations with 30 % obstacle density, it generates vehicle routes 35 % shorter than those produced by Grey Wolf Optimization and 25 % shorter than adaptive HHO, while preserving smooth, collision‑free trajectories. These results confirm that the proposed dual‑mechanism delivers fast, high‑quality solutions for high‑dimensional, dynamic path‑planning and other complex engineering optimization tasks.
A R T I C L E I N F O
Keywords • Dynamic path planning; Harris Hawks optimization; Deep reinforcement learning; Autonomous vehicles; Dynamic weight fusion; Bidirectional feedback; Intelligent transportation systems; Real-time navigation
Corresponding author • Liu, F.
Article history • Received 23 January 2025, Revised 28 May 2025, Accepted 19 June 2025
Published on-line • 31 October 2025
E X P O R T C I T A T I O N
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