1682 KB0 KB201420252025Chen, P.Liu, F.Yin, Y.Yuan, X.Zou, Q.letnik:20številka:3str. 391-414DOI:10.14743/apem2025.3.548ISSN:1854-6250COBISSID_HOST:265854211URN:URN:NBN:SI:doc-4O6JKPZFenFakulteta za strojništvo, Inštitut za proizvodno strojništvoAdvances in production engineering and managementautonomous vehiclesbidirectional feedbackdeep reinforcement learningdinamično planiranjedynamic path planningdynamic weight fusionHarris Hawks optimizationinteligentni transportni sistemiintelligent transportation systemsoptimizacijareal-time navigationDynamic Harris Hawks optimization and deep reinforcement learning framework for autonomous vehicle path planning|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 tasksTEXTznanstveno časopisjejournalsSlovenian National E-content AggregatorNational and University Library of SloveniaUniverza v Mariboru, Fakulteta za strojništvo