This study introduces a novel population-based metaheuristic algorithm called secretary bird optimization algorithm (SBOA), inspired by the survival behavior of secretary birds in their natural environment. Survival for secretary birds involves continuous hunting for prey and evading pursuit from predators. This information is crucial for proposing a new metaheuristic algorithm that utilizes the survival abilities of secretary birds to address real-world optimization problems. The algorithm's exploration phase simulates secretary birds hunting snakes, while the exploitation phase models their escape from predators. During this phase, secretary birds observe the environment and choose the most suitable way to reach a secure refuge. These two phases are iteratively repeated, subject to termination criteria, to find the optimal solution to the optimization problem. To validate the performance of SBOA, experiments were conducted to assess convergence speed, convergence behavior, and other relevant aspects. Furthermore, we compared SBOA with 15 advanced algorithms using the CEC-2017 and CEC-2022 benchmark suites. All test results consistently demonstrated the outstanding performance of SBOA in terms of solution quality, convergence speed, and stability. Lastly, SBOA was employed to tackle 12 constrained engineering design problems and perform three-dimensional path planning for Unmanned Aerial Vehicles. The results demonstrate that, compared to contrasted optimizers, the proposed SBOA can find better solutions at a faster pace, showcasing its significant potential in addressing real-world optimization problems.

In response to the challenges faced by the Coati Optimization Algorithm (COA), including imbalance between exploration and exploitation, slow convergence speed, susceptibility to local optima, and low convergence accuracy, this paper introduces an enhanced variant termed the Adaptive Coati Optimization Algorithm (ACOA). ACOA achieves a balanced exploration–exploitation trade-off through refined exploration strategies and developmental methodologies. It integrates chaos mapping to enhance randomness and global search capabilities and incorporates a dynamic antagonistic learning approach employing random protons to mitigate premature convergence, thereby enhancing algorithmic robustness. Additionally, to prevent entrapment in local optima, ACOA introduces an Adaptive Levy Flight strategy to maintain population diversity, thereby improving convergence accuracy. Furthermore, underperforming individuals are eliminated using a cosine disturbance-based differential evolution strategy to enhance the overall quality of the population. The efficacy of ACOA is assessed across four dimensions: population diversity, exploration–exploitation balance, convergence characteristics, and diverse strategy variations. Ablation experiments further validate the effectiveness of individual strategy modules. Experimental results on CEC-2017 and CEC-2022 benchmarks, along with Wilcoxon rank-sum tests, demonstrate superior performance of ACOA compared to COA and other state-of-the-art optimization algorithms. Finally, ACOA’s applicability and superiority are reaffirmed through experimentation on five real-world engineering challenges and a complex urban three-dimensional unmanned aerial vehicle (UAV) path planning problem.