To tackle the navigation challenge in dynamic and complex environments, this study designs a fusion planning framework that synergistically integrates enhanced A* algorithm with improved DWA, inspired by the biological dual-layer navigation mechanism of global path planning and local real-time obstacle avoidance. Firstly, the original global path from the conventional A* algorithm is smoothed and length-reduced through a three-stage optimization strategy involving redundant node removal and forward and reverse path relaxation, mimicking the behavioral logic of honeybees and desert ants that eliminate redundant routes to complete foraging and homing with minimal energy consumption. Secondly, an evaluation function integrating dynamic obstacle perception and adaptive weight adjustment is designed for the DWA to enhance the intelligence of local planning, drawing on the adaptive strategy of animals such as antelopes that adjust behavioral priorities according to environmental complexity to balance safety and efficiency. To comprehensively verify the performance of the proposed algorithm, simulation evaluations are performed in various scenarios, including 20 × 20 and 30 × 30 grid maps, with single and dual dynamic obstacles. Results demonstrate that our algorithm outperforms conventional methods in path length, smoothness, and safety. Further physical verification is carried out on a LiDAR-equipped mobile robot (Shenzhen Yuanchuangxing Technology Co., Ltd., Shenzhen, China) based on the ROS platform, confirming that the algorithm can stably achieve static path tracking and real-time obstacle avoidance in real indoor environments. Consequently, the developed hybrid algorithm delivers a viable and robust solution for autonomous mobile robots to navigate safely and efficiently in unpredictable and complex environments.

This study proposes Enhanced Beaver Behavior Optimizer (EBBO) to overcome the original BBO algorithm’s limitations in handling complex optimization problems. EBBO integrates a three-phase cooperative framework, incorporating adaptive mutation, dynamic opposition-based learning, and an risk-aware decision strategy inspired by simulated annealing. Comprehensive evaluations on the CEC 2017 and CEC 2020 benchmark suites demonstrate that EBBO significantly outperforms nine widely used algorithms (e.g., BBO, FATA, DE) in convergence accuracy, stability, and robustness, especially for high-dimensional and multimodal functions. EBBO achieves average objective value reductions of 15–50% and standard deviation reductions of 30–70% compared to the original BBO, with Wilcoxon rank-sum tests confirming statistical significance across most functions. When applied to three classical engineering design problems—step-cone pulley, pressure vessel, three-bar truss optimization, and 3D UAV path planning—EBBO consistently achieved the best or near-optimal solutions while satisfying all nonlinear constraints. The results confirm that EBBO effectively balances exploration and exploitation, offering a reliable and efficient approach for solving complex constrained optimization challenges in both benchmark and real-world engineering contexts.

This paper proposes an Enhanced Projection-Iterative-Methods-based Optimizer (EPIMO) to overcome the limitations of its predecessor, the Projection-Iterative-Methods-based Optimizer (PIMO), including deterministic parameter decay, insufficient diversity maintenance, and static exploration–exploitation balance. The enhancements incorporate three core strategies: (1) an adaptive decay strategy that introduces stochastic perturbations into the step-size evolution; (2) a mirror opposition-based learning strategy to actively inject structured population diversity; and (3) an adaptive adjustment mechanism for the Lévy flight parameter $\beta$ to enable phase-sensitive optimization behavior. The effectiveness of EPIMO is validated through a multi-stage experimental framework. Systematic evaluations on the CEC 2017 and CEC 2022 benchmark suites, alongside four classical engineering optimization problems (Himmelblau function, step-cone pulley design, hydrostatic thrust bearing design, and three-bar truss design), demonstrate its comprehensive superiority. The Wilcoxon rank-sum test confirms statistically significant performance improvements over its predecessor (PIMO) and a range of state-of-the-art and classical algorithms. EPIMO exhibits exceptional performance in convergence accuracy, stability, robustness, and constraint-handling capability, establishing it as a highly reliable and efficient metaheuristic optimizer. This research contributes a systematic, adaptive enhancement framework for projection-based metaheuristics, which can be generalized to improve other swarm intelligence systems when facing complex, constrained, and high-dimensional engineering optimization tasks.