An antenna array application with high directivity and low sidelobe level (SLL) is an important technology that can enhance the reliability and validity of a communication system. In recent years, swarm intelligence optimisation algorithms have been widely used in the design of antenna arrays. In this study, a hybrid heuristic swarm intelligence optimisation algorithm called cuckoo search–chicken swarm optimisation (CSCSO) is proposed to optimise the excitation amplitude of a linear antenna array (LAA) and the excitation amplitude and spacing between the array elements of a circular antenna array (CAA). The maximum SLL will be obtained while the mainlobe width is fixed. CSCSO combines cuckoo search (CS) and chicken swarm optimisation (CSO) and thus, it will have the excellent global search capability of CS as well as introduce the hierarchy mechanism of CSO to improve algorithm precision. Chaos theory is utilised to determine the initial solution of the algorithm, while Levy flight weight coefficient is used to improve the convergence rate in CSCSO. Simulation results show that CSCSO has a better performance in terms of solution accuracy and convergence rate in the radiation pattern optimisation of LAA and CAA compared with the standard CSO, CS, and particle swarm optimisation algorithms.

Feature selection is an effective method to eliminate irrelevant, redundant and noisy features, which improves the performance of classification and reduces the computational burden in machine learning. In this paper, an improved binary dragonfly algorithm (IBDA) which extends from the conventional dragonfly algorithm (DA) is proposed as a search strategy to design a wrapper-based feature selection method. First, a novel evolutionary population dynamics (EPD) strategy is introduced in IBDA to enhance the exploitation ability while ensuring population diversity of the algorithm. Second, IBDA proposes a novel crossover operator which establishes connections between the crossover rates and iterations so that making the algorithm can adjust the crossover rates of solutions dynamically, thereby balancing the exploitation and exploration of the algorithm. Finally, a binary mechanism is proposed to make the algorithm suitable for the binary feature selection problems. Simulations are conducted on 27 classical datasets from the UC Irvine Machine Learning Repository, and the results demonstrate that the proposed IBDA has better performance than some other comparison algorithms. Moreover, the effectiveness and performance of the proposed improved factors are evaluated by tests.

To improve the accuracy of user implicit rating prediction, we combine the traditional latent factor model (LFM) and bidirectional gated recurrent unit neural network (BiGRU) model to propose a hybrid model that deeply mines the latent semantics in the unstructured content of the text and generates a more accurate rating matrix. First, we utilize the user's historical behavior (favorites records) to build a user rating matrix and decompose the matrix to obtain the latent factor vectors of users and literature. We also apply the BERT model for word embedding of the research papers to obtain the sequence of word vectors. Then, we apply the BiGRU with the user attention mechanism to mine the research paper textual content and to generate the new literature latent feature vectors that are used to replace the original literature latent factor vectors decomposed from the rating matrix. Finally, a new rating matrix is generated to obtain users' ratings of noninteractive research papers and to generate the recommendation list according to the user latent factor vector. We design experiments on the real datasets and verify that the research paper recommendation model is superior to traditional recommendation models in terms of precision, recall, F1-value, coverage, popularity and diversity.INDEX TERMS recommender systems, deep learning, LFM, BiGRU, user attention I. INTRODUCTION