The sine and cosine algorithm is a new simple and effective population optimization method proposed in recent years that has been studied in many works of literature. Based on the basic principle of the sine and cosine algorithm, this paper fully studies the main parameters affecting the performance of the sine and cosine algorithm, integrates the reverse learning algorithm, adds an elite opposition solution and forms the hybrid sine and cosine algorithm (hybrid SCA). Combined with the fuzzy k-nearest neighbor method and the hybrid SCA, this paper numerically simulates two-class datasets and multi-class datasets, obtains a large number of numerical results and analyzes the results. The hybrid SCA FKNN proposed in this paper has achieved good accuracy in classification and prediction results under 10 different types of data sets. Compared with SCA FKNN, LSCA FKNN, BA FKNN, PSO FKNN and SSA FKNN, the prediction accuracy is significantly improved. In the Wilcoxon signed rank test with SCA FKNN and LSCA FKNN, the zero hypothesis (significance level 0.05) is rejected and the two classifiers have a significantly different accuracy.

The primary objective of introducing metaheuristic algorithms into traditional systematic logic is to minimize the cost function. However, there is a lack of research on the impact of introducing metaheuristic algorithms on the cost function under different proportions of positive literals. In order to fill in this gap and improve the efficiency of the metaheuristic algorithm in systematic logic, we proposed a metaheuristic algorithm based on mutation tabu search and embedded it in probabilistic satisfiability logic in discrete Hopfield neural networks. Based on the traditional tabu search algorithm, the mutation operators of the genetic algorithm were combined to improve its global search ability during the learning phase and ensure that the cost function of the systematic logic converged to zero at different proportions of positive literals. Additionally, further optimization was carried out in the retrieval phase to enhance the diversity of solutions. Compared with nine other metaheuristic algorithms and exhaustive search algorithms, the proposed algorithm was superior to other algorithms in terms of time complexity and global convergence, and showed higher efficiency in the search solutions at the binary search space, consolidated the efficiency of systematic logic in the learning phase, and significantly improved the diversity of the global solution in the retrieval phase of systematic logic.

In the realm of activity recognition, the Fuzzy K-Nearest Neighbors (FKNN) algorithm stands as a pivotal technique for classification tasks. However, the performance of FKNN is heavily reliant on the choice of its hyperparameters, notably the number of neighbors (k) and the fuzziness parameter (m). Optimizing these hyperparameters is a non-trivial challenge, significantly affecting classification accuracy. To address this challenge, an integrated approach that combines Principal Component Analysis (PCA) and an adaptive grid search algorithm for hyperparameter tuning is proposed. Utilizing a dataset captured from a Wireless Sensor Network (WSN) that measures Received Signal Strength (RSS) from sensor nodes placed on a user’s chest and ankles, the methodology initiates with preprocessing to extract time-domain features, such as mean and variance, from the raw RSS data. Strategic ranges for hyperparameters are defined to effectively explore the parameter space. PCA is employed to reduce dimensionality and enhance data structure visibility. The core of the approach lies in adaptive grid search, which iteratively refines the hyperparameter search space based on observed performance metrics, converging on optimal values that yield the highest classification accuracy. The FKNN model, trained with these optimized parameters, is then evaluated on a separate test set. Results demonstrate that this approach not only enhances the classification accuracy of the FKNN algorithm but also achieves a balanced performance in activity recognition using multisensor data fusion.

This paper proposes a classification methodology that combines neighborhood rough set feature selection with the fuzzy k-nearest neighbor algorithm. The method enhances the accuracy by integrating the fuzzy k-nearest neighbor classifier into the well-established principles of the improved neighborhood rough set feature selection. Through rigorous experimentation, we applied the proposed classification approach to numerically simulate diverse datasets across various application scenarios. The obtained results consistently showcase the high numerical performance achieved by the method. Furthermore, a comprehensive comparative analysis with baseline methods further validates the effectiveness of our approach.

The Sparrow search algorithm is a new and effective swarm intelligence method proposed in recent years and studied in many publications. Based on the basic principle of sparrow search algorithm, this paper combines the inverse learning algorithm with the refined inverse solution to form an improved sparrow search (SSA) algorithm. Combining the fuzzy k-nearest neighbor method and the improved SSA, the numerical simulation of two-classes datasets and multi-classes datasets is carried out, and many numerical results are obtained, and the results are analyzed. At the same time, this paper lists the data comparison results and tables with other models. The hybrid SSA-FKNN proposed in this paper has a clear advantage in terms of accuracy (ACC).