The bionics-based swarm intelligence optimization algorithm is a typical natural heuristic algorithm whose goal is to find the global optimal solution of the optimization problem. It simulates the group behavior of various animals and uses the information exchange and cooperation between individuals to achieve optimal goals through simple and effective interaction with experienced and intelligent individuals. This paper first introduces the principles of various swarm intelligent optimization algorithms. Then, the typical application of these swarm intelligence optimization algorithms in various fields is listed. After that, the advantages and defects of all swarm intelligence optimization algorithms are summarized. Next, the improvement strategies of various swarm intelligence optimization algorithms are explained. Finally, the future development of various swarm intelligence optimization algorithms is prospected.

While solving unimodal function problems, conventional meta-heuristic algorithms often suffer from low accuracy and slow convergence. Therefore, in this paper, a novel meta-heuristic optimization algorithm, named proton-electron swarm (PES), is proposed based on physical rules. This algorithm simulates the physical phenomena of like-charges repelling each other while opposite charges attracting in protons and electrons, and establishes a mathematical model to realize the optimization process. By balancing the global exploration and local exploitation ability, this algorithm achieves high accuracy and avoids falling into local optimum when solving target problem. In order to evaluate the effectiveness of this algorithm, 23 classical benchmark functions were selected for comparative experiments. Experimental results show that, compared with the contrast algorithms, the proposed algorithm cannot only obtain higher accuracy and convergence speed in solving unimodal function problems, but also maintain strong optimization ability in solving multimodal function problems.

In order to detect and cancel the self-interference (SI) signal from desired binary phase-shift keying (BPSK) signal, the polarization-based optimal detection (POD) scheme for cancellation of digital SI in a full-duplex (FD) system is proposed. The POD scheme exploits the polarization domain to isolate the desired signal from the SI signal and then cancel the SI to obtain the interference-free desired signal at the receiver. In FD communication, after antenna and analog cancellation, the receiver still contains residual SI due to non-linearities of hardware imperfections. In POD scheme, a likelihood ratio expression is obtained, which isolates and detects SI bits from the desired bits. After isolation of these signal points, the POD scheme cancels the residual SI. As compared to the conventional schemes, the proposed POD scheme gives significantly low bit error rate (BER), a clear constellation diagram to obtain the boundary between desired and SI signal points, and increases the receiver's SI cancellation performance in low signal to interference ratio (SIR) environment.

At present, there is an urgent need for blockchain interoperability technology to realize interconnection between various blockchains, data communication and value transfer between blockchains, so as to break the ‘ value silo’ phenomenon of each blockchain. Firstly, it lists what people understand about the concept of interoperability. Secondly, it gives the key technical issues of cross-chain, including cross-chain mechanism, interoperability, eventual consistency, and universality. Then, the implementation of each cross-chain key technology is analyzed, including Hash-locking, two-way peg, notary schemes, relay chain scheme, cross-chain protocol, and global identity system. Immediately after that, five typical cross-chain systems are introduced and comparative analysis is made. In addition, two examples of cross-chain programmability and their analysis are given. Finally, the current state of cross-chain technology is summarized from two aspects: key technology implementation and cross-chain application enforcement. The cross-chain technology as a whole has formed a centralized fixed mechanism, as well as a trend of modular design, and some of the solutions to mature applications were established in the relevant standards organizations, and the cross-chain technology architecture tends to be unified, which is expected to accelerate the evolution of the open cross-chain network that supports the real needs of the interconnection of all chains.

Human motion prediction is a critical issue in human-robot collaboration (HRC) tasks. In order to reduce thelocal error caused by the limitation of the capture range and sampling frequency of the depth sensor, a hybrid human motion prediction algorithm, optimized sliding window polynomial fitting and recursive least squares (OSWPF-RLS) was proposed. The OSWPF-RLS algorithm uses the human body joint data obtained under the HRC task as input, and uses recursive least squares (RLS) to predict the human movement trajectories within the time window. Then, the optimized sliding window polynomial fitting (OSWPF) is used to calculate the multi-step prediction value, and the increment of multi-step prediction value was appropriately constrained. Experimental results show that compared with the existing benchmark algorithms, the OSWPF-RLS algorithm improved the multi-

step prediction accuracy of human motion and enhanced the ability to respond to different human movements.  

In order to meet various challenges in the Internet of things (IoT), such as identity authentication, privacy preservation of distributed data and network security, the integration of blockchain and IoT became a new trend in recent years. As the key supporting technology of blockchain, the consensus algorithm is a hotspot of distributed system research. At present, the research direction of the consensus algorithm is mainly focused on improving throughput and reducing delay. However, when blockchain is applied to IoT scenario, the storage capacity of lightweight IoT devices is limited, and the normal operations of blockchain system cannot be guaranteed. To solve this problem, an improved version of Raft (Imp Raft) based on Raft and the storage compression consensus (SCC) algorithm is proposed, where initialization process and compression process are added into the flow of Raft. Moreover, the data validation process aims to ensure that blockchain data cannot be tampered with. It is obtained from experiments and analysis that the new proposed algorithm can effectively reduce the size of the blockchain and the storage burden of lightweight IoT devices.

Harris hawks optimization ( HHO) algorithm is an efficient method of solving function optimization problems.
However, it is still confronted with some limitations in terms of low precision, low convergence speed and stagnation
to local optimum. To this end, an improved HHO ( IHHO) algorithm based on good point set and nonlinear
convergence formula is proposed. First, a good point set is used to initialize the positions of the population
uniformly and randomly in the whole search area. Second, a nonlinear exponential convergence formula is designed
to balance exploration stage and exploitation stage of IHHO algorithm, aiming to find all the areas containing the
solutions more comprehensively and accurately. The proposed IHHO algorithm tests 17 functions and uses Wilcoxon
test to verify the effectiveness. The results indicate that IHHO algorithm not only has faster convergence speed than
other comparative algorithms, but also improves the accuracy of solution effectively and enhances its robustness
under low dimensional and high dimensional conditions.  

Aiming at the shortcomings of current gesture tracking methods in accuracy and speed, based on deep learning You Only Look Once version 4 (YOLOv4) model, a new YOLOv4 model combined with Kalman filter rea-time hand tracking method was proposed. The new algorithm can address some problems existing in hand tracking technology such as detection speed, accuracy and stability. The convolutional neural network (CNN) model YOLOv4 is used to detect the target of current frame tracking and Kalman filter is applied to predict the next position and bounding box size of the target according to its current position. The detected target is tracked by comparing the estimated result with the detected target in the next frame and, finally, the real-time hand movement track is displayed. The experimental results validate the proposed algorithm with the overall success rate of 99.43%

at speed of 41.822 frame/ s, achieving superior results than other algorithms. 

Massive multiple input multiple output (MIMO) systems can increase capacity and reliability greatly. However,  extremely high hardware costs and computational complexity lead to the demand for reasonable antenna selection.  Aiming at the problem that the traditional antenna selection algorithm based on maximizing sum capacity has large  complexity and worse bit error rate (BER) performance, a two-step selection algorithm is proposed, which selects  a part of the antennas based on the norm-based antenna selection (NBS) firstly, and then selects the antenna based  on maximizing capacity via convex optimization. The simulation results show that the improved algorithm has better  BER performance than the traditional algorithms. At the same time, it reduces computational complexity greatly.

Moving data from cloud to the edge network can effectively reduce traffic burden on the core network, and edge collaboration can further improve the edge caching capacity and the quality of service ( QoS). However, it is difficult for various edge caching devices to cooperate due to the lack of trust and the existence of malicious nodes. In this paper,blockchain which has the distributed and immutable characteristics is utilized to build a trustworthy collaborative edge caching scheme to make full use of the storage resources of various edge devices. The collaboration process is described in this paper, and a proof of credit (PoC) protocol is proposed, in which credit and tokens are used to encourage nodes to cache and transmit more content in honest behavior. Untrusted nodes will pay for their malicious actions such as tampering or deleting cached data. Since each node chooses strategy independently to maximize its benefits in an environment of mutual influence, a non-cooperative game model is designed to study the caching behavior among edge nodes. The existence of Nash equilibrium (NE) is proved in this game, so the edge server (ES) can choose the optimal caching strategy for all collaborative devices, including itself, to obtain the maximum rewards. Simulation results show that the system can save mining overhead as well as organize a trusted collaborative edge caching effectively.  

Data island and information opacity are two major problems in collaborative administration. Blockchain has the potential to provide a trustable and transparent environment encouraging data sharing among administration members. However, the blockchain only stores Hash values and transactions in blocks which makes it unable to store big data and trace their changes. In this paper, a labor arbitration scheme based on blockchain was proposed to share labor arbitration data. In the system, a collaborative administration scheme that provides a big data storage model combined blockchain and interplanetary file system ( IPFS) is designed. It can store big data and share these data among different parties. Moreover, a file version control mechanism based on blockchain is designed to manage the data changes in IPFS network. It creates a tracing chain that consists of many IPFS objects to track changes of stored data. The relationship of previous and current IPFS objects recorded by blockchain can describe the changes of administration data and trace the data operations. The proposed platform is used in Rizhao City in China, and the experiment result shows collaborative administration scheme achieves traceability with high throughput and is more efficient than traditional hypertext transfer protocol ( HTTP) way to share data.

Network traffic classification, which matches network traffic for a specific class of different granularities, plays a vital role in the domain of network administration and cyber security. With the rapid development of network communication techniques, more and more network applications adopt encryption techniques during communication, which brings significant challenges to traditional network traffic classification methods. On the one hand, traditional methods mainly depend on matching features on the application layer of the ISO/OSI reference model, which leads to the failure of classifying encrypted traffic. On the other hand, machine learning-based methods require human-made features from network traffic data by human experts, which renders it difficult for them to deal with complex network protocols. In this paper, the convolution attention network (CAT) is proposed to overcom those difficulties. As an end-to-end model, CAT takes raw data as input and returns classification results automatically, with engineering by human experts. In CAT, firstly, the importance of different bytes with an attention mechanism of network traffic is achieved. Then, convolution neural network (CNN) is used to learn features automatically and feed the output into a softmax function to get classification results. It enables CAT to learn enough information from network traffic data and ensure the classified accuracy. Extensive experiments on the public encrypted network traffic dataset ISCX2016 demonstrate the effectiveness of the proposed model.

In order to improve the learning speed and reduce computational complexity of twin support vector hypersphere (TSVH), this paper presents a smoothed twin support vector hypersphere (STSVH) based on the smoothing technique. STSVH can generate two hyperspheres with each one covering as many samples as possible from the same class respectively. Additionally, STSVH only solves a pair of unconstraint differentiable quadratic programming problems (QPPs) rather than a pair of constraint dual QPPs which makes STSVH faster than the TSVH. By considering the differentiable characteristics of STSVH, a fast Newton-Armijo algorithm is used for solving STSVH. Numerical experiment results on normally distributed clustered datasets ( NDC) as well as University of California Irvine (UCI) data sets indicate that the significant advantages of the proposed STSVH in terms of efficiency and generalization performance.

Existing algorithms of news recommendations lack in depth analysis of news texts and timeliness. To address these issues, an algorithm for news recommendations based on time factor and word embedding ( TFWE) was proposed to improve the interpretability and precision of news recommendations. First, TFWE used term frequency- inverse document frequency ( TF-IDF ) to extract news feature words and used the bidirectional encoder representations from transformers ( BERT ) pre-training model to convert the feature words into vector representations. By calculating the distance between the vectors, TFWE analyzed the semantic similarity to construct a user interest model. Second, considering the timeliness of news, a method of calculating news popularity by integrating time factors into the similarity calculation was proposed. Finally, TFWE combined the similarity of news content with the similarity of collaborative filtering ( CF) and recommended some news with higher rankings to users. In addition, results of the experiments on real dataset showed that TFWE significantly improved precision, recall, and F1 score compared to the classic hybrid recommendation algorithm.

Internet of things ( IoT) can provide the function of product traceability for industrial systems. Emerging  blockchain technology can solve the problem that the current industrial Internet of things ( IIoT) system lacks  unified product data sharing services. Blockchain technology based on the directed acyclic graph (DAG) structure  is more suitable for high concurrency environments. But due to its distributed architecture foundation, direct storage  of product data will cause authentication problems in data management. In response, IIoT based on DAG  blockchain is proposed in this paper, which can provide efficient data management for product data stored on DAG  blockchain, and an authentication scheme suitable for this structure is given. The security of the scheme is based  on a discrete-logarithm-based assumption put forth by Lysyanskaya, Rivest, Sahai and Wolf(LRSW) who also show  that it holds for generic groups. The sequential aggregation signature scheme is more secure and efficient, and the  new scheme is safe in theory and it is more efficient in engineering.

Aiming at the shortcomings of current gesture tracking methods in accuracy and speed, based on deep learning You Only Look Once version 4 (YOLOv4) model, a new YOLOv4 model combined with Kalman filter rea-time hand tracking method was proposed. The new algorithm can address some problems existing in hand tracking technology such as detection speed, accuracy and stability. The convolutional neural network (CNN) model YOLOv4 is used to detect the target of current frame tracking and Kalman filter is applied to predict the next position and bounding box size of the target according to its current position. The detected target is tracked by comparing the estimated result with the detected target in the next frame and, finally, the real-time hand movement track is displayed. The experimental results validate the proposed algorithm with the overall success rate of 99.43%

at speed of 41.822 frame/ s, achieving superior results than other algorithms. 

With the expansion of wind speed data sets, decreasing model training time is of great significance to the time cost of wind speed prediction. And imperfection of the model evaluation system also affect the wind speed prediction. To address these challenges, a hybrid method based on feature extraction, nested shared weight long short-term memory (NSWLSTM) network and Gaussian process regression (GPR) was proposed. The feature extraction of wind speed promises the best performance of the model. NSWLSTM model reduces the training time of long short-term memory (LSTM) network and improves the prediction accuracy. Besides, it adopted a method combined NSWLSTM with GPR (NSWLSTMGPR) to provide the probabilistic prediction of wind speed. The probabilistic prediction can provide information that deviates from the predicted value, which is conducive to risk assessment and optimal scheduling. The simulation results show that the proposed method can obtain high-precision point prediction, appropriate prediction interval and reliable probabilistic prediction results with shorter training time on the wind speed prediction.

Unmanned aerial vehicle base stations ( UAV-BSs) can provide a fast network deployment scheme for heterogeneous networks. However, unmanned aerial vehicle (UAV) has limited capability and cannot assist the base station (BS) well. The ability of a UAV to assist the BSs is limited, and the cluster deployment relies on the leading UAV. The dispersive deployment of multiple UAVs (multi-UAVs) need a macro base station (MBS) to determine their positions to prevent collisions or interference. Therefore, a distributed cooperative deployment scheme is proposed for UAVs to solve this problem. The scheme can increase the ability of UAVs to assist users and reduce the pressure on BSs to deploy UAVs. Firstly, the randomly distributed users are pre-clustered. Then the placement problem was modeled as a circle expansion problem and a pre-clustering radius expansion algorithm was proposed. Under the constraint of users-data rates, it provides services for more users. Finally, the proposed algorithm was compared with the density-aware placement algorithm. The simulation results show that the proposed algorithm can provide services for more users and improve the coverage rate of users while ensuring the data rates.

To improve the anti-noise ability of fuzzy local information C-means  clustering, a robust entropy-like distance driven fuzzy clustering with local information is proposed. This paper firstly uses Jensen-Shannon divergence to induce a symmetric entropy-like divergence. Then the root of entropy-like divergence is proved to be a distance measure, and it is applied to existing fuzzy C-means (FCM) clustering to obtain a new entropy-like divergence driven fuzzy clustering, meanwhile its convergence is strictly proved by Zangwill theorem. In the end, a robust fuzzy clustering by combing local information with entropy-like distance is constructed to segment image with noise. Experimental results show that the proposed algorithm has better segmentation accuracy and robustness against noise than existing state-of-the-art fuzzy clustering-related segmentation algorithm in the presence of noise.