| Title: | Dynamic Harris Hawks optimization and deep reinforcement learning framework for autonomous vehicle path planning |
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| Authors: | ID Zou, Q. (Author)
ID Yuan, X. (Author)
ID Liu, F. (Author)
ID Yin, Y. (Author)
ID Chen, P. (Author) |
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| Files: |  APEM20-3_391-414.pdf (1,64 MB)
MD5: F627A2A8C694CA5C15EE3F43FE2E4C62
 https://apem-journal.org/Archives/2025/Abstract-APEM20-3_391-414.html
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| Language: | English |
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| Work type: | Article |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FS - Faculty of Mechanical Engineering
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| Abstract: | 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. |
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| Keywords: | dynamic path planning, Harris Hawks optimization, deep reinforcement learning, autonomous vehicles, dynamic weight fusion, bidirectional feedback, intelligent transportation systems, real-time navigation |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 23.01.2025 |
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| Article acceptance date: | 19.06.2025 |
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| Publication date: | 31.10.2025 |
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| Publisher: | Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering |
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| Year of publishing: | 2025 |
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| Number of pages: | str. 391-414 |
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| Numbering: | Vol. 20, no. 3 |
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| PID: | 20.500.12556/DKUM-96686  |
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| UDC: | 658.5 |
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| ISSN on article: | 1854-6250 |
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| COBISS.SI-ID: | 265854211 |
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| DOI: | 10.14743/apem2025.3.548 |
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| Publication date in DKUM: | 23.01.2026 |
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| Views: | 154 |
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| Downloads: | 8 |
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| Metadata: |    |
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| Categories: | Misc.
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