Structural vibration control is a critical challenge in engineering systems such as tower cranes, where excessive oscillations compromise safety and operational accuracy. This paper proposes a Fertilized Particle Swarm Optimization (FRPSO) algorithm that hybridizes Particle Swarm Optimization with Flower Fertilization Optimization via a dual-phase global-best update to enhance the exploration–exploitation balance. FRPSO is evaluated on 26 problems (20 non-convex constrained cases, four 1000-variable large-scale benchmarks, structural optimization, and a tower crane vibration-control case study) and is compared against 18 metaheuristic optimizers. Across the benchmark suites, FRPSO achieves solution quality with consistent run-to-run stability, achieving near-optimal objective values with very low dispersion in the 1000-variable tests under the reported experimental protocol. In structural optimization, FRPSO reduces the weight of the 72-bar truss from 381.91 lb (PSO) to 379.63 lb. For the tower crane boom modeled as a 3D beam structure under transient dynamic loading, FRPSO yields designs that achieve effective vibration attenuation, as evidenced by the rapid decay of boom-tip vertical displacement responses, while satisfying stress and displacement constraints. Non-parametric statistical comparisons based on the Wilcoxon signed-rank test indicate that the observed improvements are consistent across repeated runs for selected benchmark cases. Overall, the reported results suggest that FRPSO can be effectively applied to vibration-aware structural design of crane booms and other flexible beam-type structures.

This work presents a generalized framework for solving the inverse kinematics problem of robotic manipulators by introducing two approaches: optimization-based and learning-based approaches within a unified architecture. The optimization-based inverse kinematic solver is formulated as a minimization problem of the error in localization of the end-effector. On the other hand, the artificial neural network’s inverse kinematic solver is presented as a subsequent operation to a given straightforward forward kinematics analysis. The two proposed formulations are applicable to robot manipulators with any synthesis and degrees of freedom. To validate the proposed solvers, a low-cost 6-DOF robotic platform was developed, including a host-embedded system that enables real-time interaction between a virtual environment and a physical manipulator. Different optimization algorithms and different learning algorithms were used and their effect was compared to evaluate the efficiency of the proposed framework. The two approaches were analyzed and compared with respect to accuracy, computational cost, and real-time suitability. Results show that optimization methods provide higher precision and require longer computation time, whereas the artificial neural network-based method achieves significantly faster responses with acceptable approximation error. Experimental validation demonstrates the robustness and practical applicability of the framework, which is recommended for high-degree-of-freedom manipulators where analytical and closed-form solutions do not exist.