Research and Validation of Complex Constrained Path Planning Based on the Multi-Strategy Improved Aquila Optimizer

Abstract

To address the inherent limitations of the traditional Aquila Optimizer (AO)—specifically slow convergence, susceptibility to local optima, and limited high-dimensional adaptability—this study proposes a multi-strategy Improved Aquila Optimizer algorithm. Key enhancements include the integration of a logarithmically decaying tangent flight factor to optimize high-dimensional solution distributions, and a dual-layer t-distribution adaptive perturbation model to dynamically regulate search density. Additionally, to solve path-planning problems under strict constraints, we incorporate a prior feasible region initialization, a continuous-to-discrete mapping correction, and a local fine-search mechanism for trajectory smoothing. The proposed Improved Aquila Optimizer algorithm is systematically evaluated against the original AO and six popular algorithms (PSO, SSA, GWO, DBO, DE, and GA) across 23 benchmark functions, the CEC2017 suite, and multi-scale grid maps. The results demonstrate that the Improved Aquila Optimizer algorithm achieves an order-of-magnitude improvement in convergence reliability. By prioritizing absolute search stability and robustness in high-dimensional tasks, the proposed algorithm attains an optimal balance between convergence quality and practical engineering efficiency, proving exceptionally effective in complex path-planning scenarios.