Improved adaptive snake optimization algorithm with application to multi-UAV path planning

Metaheuristic swarm-based intelligent algorithms are extensively employed for engineering optimization tasks due to their efficacy in addressing nonlinear and high-dimensional challenges. This study presents an improved snake optimization algorithm (SOEA) to overcome the limitations of the standard snake optimization algorithm (SOA), such as slow convergence, subpar optimization accuracy, and vulnerability to local optima. The integration of elite opposition-based learning strategy enables the adjustment of snake population positions, thereby enhancing the algorithm’s global search capacity and iteration speed. Moreover, the incorporation of the adaptive threshold method enhances its local search performance and convergence speed. Experimental results demonstrate the superior performance of the proposed SOEA algorithm in achieving global optimization and accelerating convergence speed. The SOEA algorithm achieves a remarkable 34% reduction in the average number of iterations required compared to the SOA algorithm, and it also exhibits a lower mean squared error. Finally, the effectiveness of the proposed algorithm is validated through its successful application to solving the multi-UAV path planning problem.