This study presents an improved variant of the greater cane rat algorithm (GCRA), called adaptive and global-guided greater cane rat algorithm (AGG-GCRA), which aims to alleviate some key limitations of the original GCRA regarding convergence speed, solution precision, and stability. GCRA simulates the foraging behavior of the greater cane rat during both mating and non-mating seasons, demonstrating intelligent exploration capabilities. However, the original algorithm still faces challenges such as premature convergence and inadequate local exploitation when applied to complex optimization problems. To address these issues, this paper introduces four key improvements to the GCRA: (1) a global optimum guidance term to enhance the convergence directionality; (2) a flexible parameter adjustment system designed to maintain a dynamic balance between exploration and exploitation; (3) a mechanism for retaining top-quality solutions to ensure the preservation of optimal results.; and (4) a local perturbation mechanism to help escape local optima. To comprehensively evaluate the optimization performance of AGG-GCRA, 20 separate experiments were carried out across 26 standard benchmark functions and six real-world engineering optimization problems, with comparisons made against 11 advanced metaheuristic optimization methods. The findings indicate that AGG-GCRA surpasses the competing algorithms in aspects of convergence rate, solution precision, and robustness. In the stability analysis, AGG-GCRA consistently obtained the global optimal solution in multiple runs for five engineering cases, achieving an average rank of first place and a standard deviation close to zero, highlighting its exceptional global search capabilities and excellent repeatability. Statistical tests, including the Friedman ranking and Wilcoxon signed-rank tests, provide additional validation for the effectiveness and importance of the proposed algorithm. In conclusion, AGG-GCRA provides an efficient and stable intelligent optimization tool for solving various optimization problems.

Teleoperated ground vehicles (TGVs) are widely applied in hazardous and dynamic environments, where communication delay and low transparency increase operator workload and reduce control performance. This study explores the cognitive and physiological workload associated with such conditions and evaluates the effectiveness of an eye-movement-based predicted trajectory guidance control (ePTGC) framework in alleviating operator burden. A human-in-the-loop teleoperation experiment was conducted using a 2 × 2 within-subject design, incorporating subjective ratings (NASA-TLX), objective performance metrics from a dual-task paradigm (one-back memory task), and multimodal physiological indicators (ECG and EDA). Results show that delay and low transparency significantly elevated subjective, objective, and physiological workload levels. Compared to direct control (DC), the ePTGC framework significantly reduced workload across all three dimensions, particularly under high-delay conditions, while maintaining or even improving task performance. Notably, ePTGC enabled even lower workload levels under low-delay conditions than the baseline condition. These findings demonstrate the potential of the ePTGC framework to enhance teleoperation stability and reduce operator burden in delay-prone and low-transparency scenarios.

This paper introduces a market-inspired meta-heuristic named the Market Game Optimization Algorithm (MGOA), which models the symmetric competitive interactions between merchants and customers. MGOA simulates the dual dynamics of attraction and collaboration among merchants and customers, integrating a global search strategy that reflects how merchants attract customers. The optimization process includes two distinct stages, each employed with a 50% probability through a merchant cooperation mechanism designed to balance exploration and exploitation. Prior to performance evaluation, a qualitative analysis and parameter sensitivity study were conducted to clarify parameter selection and optimization characteristics. The algorithm was tested on 42 benchmark functions from the CEC2017 and CEC2022 test suites and compared against 10 well-established and 10 recently proposed meta-heuristic algorithms. Results indicate that MGOA achieves the highest overall ranking. Furthermore, its application to 11 real-world engineering design problems demonstrates high effectiveness and solution quality. All experimental findings confirm that MGOA is a robust and competitive meta-heuristic capable of solving complex real-world optimization problems.