In this study, we propose a novel improved Dung Beetle Optimizer called Environment-aware Chaotic Force-field Dung Beetle Optimizer (ECFDBO). To address DBO’s existing tendency toward premature convergence and insufficient precision in high-dimensional, complex search spaces, ECFDBO integrates three key improvements: a chaotic perturbation-based nonlinear contraction strategy, an intelligent boundary-handling mechanism, and a dynamic attraction–repulsion force-field mutation. These improvements reinforce both the algorithm’s global exploration capability and its local exploitation accuracy. We conducted 30 independent runs of ECFDBO on the CEC2017 benchmark suite. Compared with seven classical and novel metaheuristic algorithms, ECFDBO achieved statistically significant improvements in multiple performance metrics. Moreover, by varying problem dimensionality, we demonstrated its robust global optimization capability for increasingly challenging tasks. We further conducted the Wilcoxon and Friedman tests to assess the significance of performance differences of the algorithms and to establish an overall ranking. Finally, ECFDBO was applied to a 3D path planning simulation in UAVs for safe path planning in complex environments. Against both the Dung Beetle Optimizer and a multi-strategy DBO (GODBO) algorithm, ECFDBO met the global optimality requirements for cooperative UAV planning and showed strong potential for high-dimensional global optimization applications.

Multi-UAV three-dimensional (3D) path planning is formulated as a high-dimensional multi-constraint optimization problem involving costs such as path length, flight altitude, avoidance cost, and smoothness. To address this challenge, we propose an Adaptive Strategy Hybrid Secretary Bird Optimization Algorithm (ASHSBOA), an enhanced variant of the Secretary Bird Optimization Algorithm (SBOA). ASHSBOA integrates a weighted multi-direction dynamic learning strategy, an adaptive strategy-selection mechanism, and a hybrid elite-guided boundary-repair scheme to enhance the ability to identify local optima and balance exploration and exploitation. The algorithm is tested on benchmark suites CEC-2017 and CEC-2022 against nine classic or state-of-the-art optimizers. Non-parametric tests show that ASHSBOA consistently achieves superior performance and ranks first among competitors. Finally, we applied ASHSBOA to a multi-UAV 3D path planning model. In Scenario 1, the path cost planned by ASHSBOA decreased by 124.9 compared to the second-ranked QHSBOA. In the more complex Scenario 2, this figure reached 1137.9. Simulation results demonstrate that ASHSBOA produces lower-cost flight paths and more stable convergence behavior compared to comparative methods. These results validate the robustness and practicality of ASHSBOA in UAV path planning.

With the increasing application of unmanned aerial vehicle (UAV) in multiple fields, the path planning problem has become a key challenge in the optimization domain. This paper proposes an Improved Artificial Lemming Algorithm (IALA), which incorporates three strategies: the optimal information retention strategy based on individual historical memory, the hybrid search strategy based on differential evolution operators, and the local refined search strategy based on directed neighborhood perturbation. These strategies are designed to enhance the algorithm’s global exploration and local exploitation capabilities in tackling complex optimization problems. Subsequently, comparative experiments are conducted on the CEC2017 benchmark suite across three dimensions (30D, 50D, and 100D) against eight state-of-the-art algorithms proposed in recent years, including SBOA and DBO. The results demonstrate that IALA achieves superior performance across multiple metrics, ranking first in both the Wilcoxon rank-sum test and the Friedman ranking test. Analyses of convergence curves and data distributions further verify its excellent optimization performance and robustness. Finally, IALA and the comparative algorithms are applied to eight 3D UAV path planning scenarios and two amphibious UAV path planning models. In the independent repeated experiments across the eight scenarios, IALA attains the optimal performance 13 times in terms of the two metrics, Mean and Std. It also ranks first in the Monte Carlo experiments for the two amphibious UAV path planning models.