This paper introduces a novel bio-inspired metaheuristic algorithm, named the sharpbelly fish optimizer (SFO), inspired by the collective ecological behaviors of the sharpbelly fish. The algorithm integrates four biologically motivated strategies—(1) fitness-driven fast swimming, (2) convergence-guided gathering, (3) stagnation-triggered dispersal, and (4) disturbance-induced escape—which synergistically enhance the balance between global exploration and local exploitation. To assess its performance, the proposed SFO is evaluated on the CEC2022 benchmark suite under various dimensions. The experimental results demonstrate that SFO consistently achieves competitive or superior optimization accuracy and convergence speed compared to seven state-of-the-art metaheuristic algorithms. Furthermore, the algorithm is applied to three classical constrained engineering design problems: pressure vessel, speed reducer, and gear train design. In these applications, SFO exhibits strong robustness and solution quality, validating its potential as a general-purpose optimization tool for complex real-world problems. These findings highlight SFO’s effectiveness in tackling nonlinear, constrained, and multimodal optimization tasks, with promising applicability in diverse engineering scenarios.

Robotic positioning accuracy is critically affected by both geometric and non-geometric errors. To address this dual error issue comprehensively, this paper proposes a novel two-stage compensation framework. First, a Memory based red billed blue magpie optimizer (MRBMO) is employed to identify and compensate for geometric errors by optimizing the geometric parameters based on end-effector observations. This memory-guided evolutionary mechanism effectively enhances the convergence accuracy and stability of the geometric calibration process. Second, a tuned graph neural network (AMPSO-GNN) is developed to model and compensate for non-geometric errors, such as cable deformation, thermal drift, and control imperfections. The GNN architecture captures the topological structure of the robotic system, while the adaptive momentum PSO dynamically optimizes the network’s hyperparameters for improved generalization. Experimental results on a six-axis industrial robot demonstrate that the proposed method significantly reduces residual positioning errors, achieving higher accuracy compared to conventional calibration and compensation strategies. This dual-compensation approach offers a scalable and robust solution for precision-critical robotic applications.

Design and Experimental Research on EEG Control System of Unmanned Vehicle Based on Brain-Computer Technology

Accurate absolute positioning is essential for industrial robot arms, especially in high-precision manufacturing tasks. Traditional calibration methods often rely heavily on domain-specific knowledge and handcrafted algorithms, making it challenging for broader adoption across disciplines. To tackle this problem, this paper proposes a novel calibration framework based on an enhanced metaheuristic approach named RLSGA, which integrates deep reinforcement learning with the Snow Geese Algorithm (SGA). Unlike conventional strategies where the movement of agents is fully determined by predefined equations, the proposed method leverages a deep policy network to guide individual geese’s migration behavior. This network generates adaptive decisions regarding position updates, convergence direction, and flight mode selection. The learned policy enables more flexible and efficient exploration of the calibration parameter space. Experimental results on robot arm calibration tasks demonstrate that RLSGA achieves superior calibration accuracy and robustness compared to existing optimization-based methods, validating its effectiveness and potential for real-world applications.