Dynamic Cooperative Whale Optimization Algorithm for Multivehicle IoV Path Planning
Wenbiao Yang, Wenli Shang, Zhiquan Liu · IEEE Internet of Things Journal · 2025
The Internet of Vehicles (IoV) presents significant challenges for path planning algorithms in dynamic traffic environments. This paper proposes the Dynamic Cooperative Whale Optimization Algorithm (DCWOA) for multi-vehicle path planning in IoV. DCWOA enhances the Whale Optimization Algorithm with a three-layer structure (Individual, Group, and Group Cooperation) to optimize from local to global scope. Key innovations include: (1) a dynamic adjustment factor combining improved encircling and spiral update mechanisms; (2) local and global cooperation mechanisms enabling coordinated planning through vehicle communications; and (3) a multi-objective weighted decision model integrating travel time, fuel consumption, safety, and emissions. Comparisons with five stateof-the-art algorithms (WOA, MEWOA, PSBES, MGO, DGCO) on the CEC2017 benchmark suite show DCWOA achieving optimal performance in 27-29 of 30 test functions. In IoV environments, DCWOA demonstrates 36% improvement in optimization efficiency at 60% traffic density and reduces travel time by 21-26%. During unexpected events, DCWOA achieves 7-second path adjustment time with 100% success rate, outperforming comparison algorithms’ 12-28 seconds and 65-85%. The code is available at: https://github.com/yangwb02/MVPP-DCWOA.