This paper presents an enhanced artificial lemming algorithm (EALA) for solving complex unmanned aircraft system (UAV) path planning problems in three-dimensional environments. Key improvements include chaotic initialization, adaptive perturbation, and hybrid mutation, enabling a better exploration–exploitation balance and local refinement. Validation on the IEEE CEC2017 and CEC2022 benchmark functions demonstrates the EALA’s superior performance, achieving faster convergence and better algorithm performance compared to the standard ALA and 10 other algorithms. When applied to UAV path planning in large- and medium-scale environments with realistic obstacle constraints, the EALA generates Pareto-optimal paths that minimize length, curvature, and computation time while guaranteeing collision avoidance. Benchmark tests and realistic simulations show that the EALA outperforms 10 algorithms. This method is particularly suited for mission-critical applications with strict safety and time constraints.

The starfish optimization algorithm (SFOA) is a metaheuristic evolutionary intelligence algorithm with a great global search capability and strong adaptability. Although the SFOA has a good global search capability, it is not accurate enough in local search and converges slowly. To further enhance this convergence ability and global optimization ability, an enhanced starfish optimization algorithm (SFOAL) is proposed that combines sine chaotic mapping, t-distribution mutation, and logarithmic spiral reverse learning. The SFOAL can remarkably enhance both the global and local convergence capabilities of the algorithm, leading to a more rapid convergence speed and greater stability. In total, 23 benchmark functions and CEC2021 were used to test the development, search, and convergence capabilities of the SFOAL. The SFOAL was compared in detail with other algorithms. The experimental results demonstrated that the overall performance of the SFOAL was better than that of other algorithms, and the joint strategy could effectively balance the development and search capabilities to obtain stronger global and local optimization capabilities. For solving practical problems, the SFOAL was used to optimize the back propagation (BP) neural network to solve the ultra-wideband line-of-sight positioning problem. The results showed that the SFOAL-BP neural network had a smaller average position error compared to the random BP neural network and the SFOA-BP neural network, so it can be used to solve practical application problems.

Intelligent optimization algorithms are crucial for solving complex engineering problems. The Parrot Optimization (PO) algorithm shows potential but has issues like local-optimum trapping and slow convergence. This study presents the Chaotic–Gaussian–Barycenter Parrot Optimization (CGBPO), a modified PO algorithm. CGBPO addresses these problems in three ways: using chaotic logistic mapping for random initialization to boost population diversity, applying Gaussian mutation to updated individual positions to avoid premature local-optimum convergence, and integrating a barycenter opposition-based learning strategy during iterations to expand the search space. Evaluated on the CEC2017 and CEC2022 benchmark suites against seven other algorithms, CGBPO outperforms them in convergence speed, solution accuracy, and stability. When applied to two practical engineering problems, CGBPO demonstrates superior adaptability and robustness. In an indoor visible light positioning simulation, CGBPO’s estimated positions are closer to the actual ones compared to PO, with the best coverage and smallest average error.

The Walrus Optimization (WO) algorithm, a metaheuristic inspired by walrus behavior, is known for its competitive convergence speed and effectiveness in solving high-dimensional and practical engineering optimization problems. However, it suffers from a tendency to converge to local optima and exhibits instability during the iterative process. To overcome these limitations, this study proposes an improved WO (IMWO) algorithm based on the integration of Differential Evolution/best/1 (DE/best/1) mutation, Logistics–Sine–Cosine (LSC) Mapping, and the Beta Opposition-Based Learning (Beta-OBL) strategy. These strategies work synergistically to enhance the algorithm’s global exploration capability, improve its search stability, and accelerate convergence with higher precision. The performance of the IMWO algorithm was comprehensively evaluated using the CEC2017 and CEC2022 benchmark test suites, where it was compared against the original WO algorithm and six other state-of-the-art metaheuristics. Experimental data revealed that the IMWO algorithm achieved average fitness rankings of 1.66 and 1.33 in the two test suites, ranking first among all compared algorithms. The WSN coverage optimization problem aims to maximize the monitored area while reducing perception blind spots under limited node resources and energy constraints, which is a typical complex optimization problem with multiple constraints. In a practical application addressing the coverage optimization problem in Wireless Sensor Networks (WSNs), the IMWO algorithm attained average coverage rates of 95.86% and 96.48% in two sets of coverage experiments, outperforming both the original WO and other compared algorithms. These results confirm the practical utility and robustness of the IMWO algorithm in solving complex real-world engineering problems.

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.