The black-winged kite algorithm (BKA) integrates the Cauchy mutation strategy and the leader selection strategy to simulate high-altitude circling exploration, fixed-point diving attack, and group cooperative migration of the black-winged kites to approximate the global optimal solution. The BKA exhibits deficiencies in ponderous convergence efficacy, inefficient calculation precision, and insufficient population diversity. To strengthen the convergence property and computational practicability, an enhanced BKA with multiple strategies (MSBKA) is advocated to accommodate global optimization and constrained engineering applications. The objective is to systematically verify its advancement and competitiveness and accurately actualize the global optimal solution. The ranking-based differential mutation can strengthen population information interaction, accelerate convergence efficiency, restrain premature convergence, diminish homogenization competition, promote exploration and exploitation, intensify elite individual guidance, downscale ineffective iterations, and materialize orderly population renewal. The simplex method can execute the local refinement operations of reflection, expansion, compression and contraction, strengthen local mining efficiency, ameliorate solution accuracy, abate parameter sensitivity, eschew local optimal traps, accelerate accurate convergence, and preserve the optimal individual potential. The elite opposition-based learning strategy can fabricate reverse solutions, expand the monolithic detection space, shorten the convergence process, elevate the quality of initial and iterative solutions, boost population diversity, guide intelligent search direction, and relieve premature convergence. The MSBKA utilizes deficiency orientation, strategy adaptation, and collaborative search to accomplish the realistic demands of high-precision, high-efficiency and strong constraint adaptation, surmount the static trade-off dilemma, endow a strong directional abscond mechanism to replace random perturbation, and actualize the inertia of directional exploration and the blind spots of solution exploitation. Twenty-three benchmark functions and six real-world engineering designs are employed to authenticate theoretical superiority and engineering practicability. The experimental results demonstrate that the MSBKA incorporates strong practicability and reliability to strengthen information interaction, restrain search stagnation, diminish convergence oscillation and fluctuation, facilitate globalized discovery and localized extraction, expedite convergence efficacy, ameliorate solution precision, and consolidate stability and robustness.

The greater cane rat algorithm (GCRA) represents an emerging swarm intelligence paradigm derived from the instinctual survival patterns exhibited by greater cane rats (GCRs), which simulates the typical male-dominated survival patterns of the GCR species, including rainy-season mating and reproduction behaviors, dry-season behavioral differentiation of solitary males and clustered females, and their nonlinear adaptive foraging characteristics. Nevertheless, the original GCRA suffers from inherent defects in complex and high-dimensional optimization scenarios, encompassing premature convergence phenomena, inadequate local exploitation proficiency, constrained convergence precision, and a proneness to stagnation at local optima, which severely restrict its practical engineering application. To address the aforementioned limitations, this work introduces an enhanced hybrid variant of the greater cane rat algorithm, amalgamated with Teaching-and-Learning-Based Optimization (TLBO) and designated as the TLGCRA, incorporating three pivotal targeted innovations. Specifically, the TLGCRA innovatively introduces the two-stage teacher–student interactive learning mechanism of TLBO on the basis of retaining the core evolutionary and behavioral characteristics of the original GCRA, which effectively compensates for the insufficient local disturbance capability of the original algorithm and enriches population diversity to avoid local optimum stagnation. Furthermore, an adaptive parameter tuning strategy is innovatively designed and embedded in the iterative optimization process, which dynamically balances the global exploration and local exploitation capabilities of the algorithm, fundamentally improving the low learning efficiency and weak mining performance of the GCRA. A suite of computational simulations is conducted across 23 canonical benchmark functions and six representative constrained engineering design optimization scenarios. The introduced TLGCRA is benchmarked against the canonical GCRA, LPSO, and ten cutting-edge metaheuristic approaches. Empirical outcomes substantiate that the TLGCRA attains marked performance advantages in terms of convergence velocity, solution precision, and algorithmic resilience. In particular, the optimized design effectively improves the optimal solution precision of the algorithm in complex multimodal function optimization, and the standard deviation of multiple independent runs in six engineering application cases is close to zero, verifying its excellent stability. Statistical verification employing the Friedman test and Wilcoxon signed-rank test additionally corroborates that the TLGCRA exhibits statistically robust and dependable optimization efficacy. In summary, the proposed innovative fusion strategies endow the TLGCRA with stronger environmental adaptability and comprehensive optimization performance, enabling it to realize faster convergence speed and higher computational accuracy, as well as outstanding stability and robustness, thus furnishing a viable resolution framework for intricate constrained engineering optimization challenges.

Surface electromyography (sEMG) signals directly reflect movement intention and are therefore promising for natural human–machine interaction. However, their inherent non-stationarity and high inter-subject variability remain major obstacles to robust feature extraction and model generalization. To address these challenges, this study proposes a dual-stream spatial pyramid pooling convolutional neural network (DSSCNN). In this framework, one-dimensional sEMG segments are transformed into two complementary image representations, continuous wavelet transform (CWT) spectrograms and Gramian angular difference field (GADF) images, forming a dual-channel input that jointly preserves time–frequency dynamics and temporal correlation structures. A dual-stream convolutional architecture then extracts discriminative features from each modality, after which a spatial pyramid pooling (SPP) layer aggregates multi-scale representations, enhancing the network’s capacity to capture robust spatiotemporal patterns. Extensive experiments demonstrate that DSSCNN achieves an average gesture recognition accuracy of 97.88% with low inter-subject variance under intra-subject random split, and 96.59% under leave-one-subject-out (LOSO) protocol. The practical viability of the proposed approach is further validated through real-time control of an unmanned ground vehicle (UGV). These results not only indicate that the dual-stream framework combined with SPP layer provides an effective strategy for high-precision sEMG-based gesture recognition but also provides a promising technical pathway toward next-generation natural human–machine interaction.

The greater cane rat algorithm (GCRA) draws inspiration from the seasonal behavioral patterns of the greater cane rats: extensive roaming during the non-breeding period for global exploration, and aggregative foraging during the reproductive period for local exploitation. The GCRA leverages independent movement and population aggregation to iteratively update positions in pursuit of the optimal solution, which exhibits inherent structural deficiencies: precipitous population diversity collapse, lethargic convergence dynamics, suboptimal computational precision, high susceptibility to local optima, and severe dimensional scalability. This paper proposes a modified complex-valued encoding GCRA (CGCRA) that exploits the mathematical structure of complex numbers to construct a two-dimensional search domain on the complex plane and facilitate collaborative optimization. The CGCRA maps the decision variables onto the complex domain, the real part executes the native foraging mechanism for local fine-grained exploitation, and the imaginary part exploits phase rotation to generate global exploratory perturbations. The CGCRA leverages a dual-encoding redundancy mechanism with inherent error tolerance to attenuate result volatility, augment information capacity and population heterogeneity, elevate search adaptability and disturbance rejection, accelerate parallel computation and exploration efficiency, and facilitate spatial transformation and multi-dimensional data manipulation. Twenty-three benchmark functions and twelve real-world engineering designs are employed to assess the CGCRA’s stability and practical feasibility rigorously. The CGCRA delivers comprehensive spatial mapping and adaptive coordination to facilitate population collaboration and bolster resilience, expedite exhaustive research, and advance optimization efficiency. The experimental results demonstrate that the CGCRA emphasizes instructive superiority and practical utility to regulate exploration and exploitation, reduce result dispersion, mitigate search stagnation, accelerate convergence efficiency, elevate solution precision, and fortify stability and robustness.