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Considering the low indoor positioning accuracy and poor positioning stability of traditional machine-learning algorithms, an indoor-fingerprint-positioning algorithm based on weighted k-nearest neighbors (WKNN) and extreme gradient boosting (XGBoost) was proposed in this study. Firstly, the outliers in the dataset of established fingerprints were removed by Gaussian filtering to enhance the data reliability. Secondly, the sample set was divided into a training set and a test set, followed by modeling using the XGBoost algorithm with the received signal strength data at each access point (AP) in the training set as the feature, and the coordinates as the label. Meanwhile, such parameters as the learning rate in the XGBoost algorithm were dynamically adjusted via the genetic algorithm (GA), and the optimal value was searched based on a fitness function. Then, the nearest neighbor set searched by the WKNN algorithm was introduced into the XGBoost model, and the final predicted coordinates were acquired after weighted fusion. As indicated in the experimental results, the average positioning error of the proposed algorithm is 1.22 m, which is 20.26–45.58% lower than that of traditional indoor positioning algorithms. In addition, the cumulative distribution function (CDF) curve can converge faster, reflecting better positioning performance. [ABSTRACT FROM AUTHOR]

This paper presents a new variant of cuckoo search (CS) algorithm named neighborhood learning-based CS (NLCS) to address global optimization problems. Specifically, in this modified version, each individual learns from the personal best solution rather than the best solution found so far in the entire population to discourage premature convergence. To further enhance the performance of CS on complex multimode problems, each individual is allowed to learn from different learning exemplars on different dimensions. Moreover, the exemplar individual is chosen from a predefined neighborhood to further maintain the population diversity. This scheme enables each individual to interact with the historical experience of its own or its neighbors, which is controlled by using a learning probability. Extensive comparative experiments are conducted on 39 benchmark functions and two application problems of neural network training. Comparison results indicate that the proposed NLCS algorithm exhibits competitive convergence performance. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the standard cuckoo search algorithm relies on Levy flights, which leads to the step-size randomness of the search process, a self-adaptive step cuckoo search algorithm based on dynamic balance factor is proposed in our paper. First, two parameters are introduced in our paper, which were iteration number ratio parameter and adaptability ratio parameter. Then, a dynamic balance factor parameter is introduced to adjust the weight number of iteration number ratio parameter and adaptability ratio parameter. Finally, parameter skewness value calculation method and self-adaptive step strategy were proposed combined with the dynamic balance factor. Six typical test functions are used to test the performance of the proposed algorithm, the standard cuckoo search algorithm and the self-adaptive step cuckoo search algorithm which relies only on the iterative times. The test results show that the proposed algorithm had good convergence speed and accuracy. Meanwhile, taking the permutation flow shop scheduling problem as an example, eight operators of Car benchmark class are used as the test data to compare the performance of three algorithms, the effectiveness and superiority of the algorithm in solving the permutation flow shop scheduling problem are verified. [ABSTRACT FROM AUTHOR]

A Cuckoo Search (CS) algorithm based on ant colony algorithm is proposed for scheduling problem in permutation flow shop scheduling problem (PFSP). When the raised CS algorithm obtains the position of the bird nest to be updated, it is used as a set of initial solution of the ant colony optimization algorithm (ACO), and ACO algorithm search optimization is performed in a very small range. After that, the solution obtained by the ACO search is taken as a new candidate solution, compared with the candidate bird nest according to the fitness degree. When the candidate solution of the ACO search optimization is better than the one generated by the Lévy flight, the latter is replaced. Finally, the CS algorithm is selected, changing the new bird nest position according to the abandonment probability. The updated position tends to be more optimal, which improves the quality of the solution as well as the convergence speed and accuracy of the algorithm. Comparing the performance of the proposed algorithm with the standard Cuckoo one, by testing function, the optimized performance was verified. Finally, the Car benchmark test served as test data, and the performance in the PFSP was compared. The effectiveness and superiority in the algorithm in solving problem were confirmed. [ABSTRACT FROM AUTHOR]