GLBWOA: A Global–Local Balanced Whale Optimization Algorithm for UAV Path Planning

To tackle the challenges of path planning for unmanned aerial vehicle (UAV) in complex environments, a global–local balanced whale optimization algorithm (GLBWOA) has been developed. Initially, to prevent the population from prematurely converging, a bubble net attack enhancement strategy is incorporated, and mutation operations are introduced at different stages of the algorithm to mitigate early convergence. Additionally, a failure parameter test mutation mechanism is integrated, along with a predefined termination rule to avoid excessive computation. The algorithm’s convergence is accelerated through mutation operations, further optimizing performance. Moreover, a random gradient-assisted optimization approach is applied, where the negative gradient direction is identified during each iteration, and an appropriate step size is selected to enhance the algorithm’s exploration capability toward finding the optimal solution. The performance of GLBWOA is benchmarked against several other algorithms, including SCA, BWO, BOA, and WOA, using the IEEE CEC2017 test functions. The results indicate that the GLBWOA outperforms other algorithms. Path-planning simulations are also conducted across four benchmark scenarios of varying complexity, revealing that the proposed algorithm achieves the lowest average total cost for flight path planning and exhibits high convergence accuracy, thus validating its reliability and superiority.