Gaussian processes (GPs) have attracted considerable attention in assisting evolutionary algorithms (EAs) to solve computationally expensive optimization problems (EOPs) because they can directly provide information about the uncertainty of their predictions. However, the computational complexity of GPs grows cubically as the amount of data increases, which severely limits their computational efficiency in high-dimensional expensive multiobjective optimization problems (EMOPs). To address this limitation, we propose a surrogate-assisted evolutionary algorithm (SAEA) that integrates multilayer perceptron (MLP) grouping with sparse GPs, referred to as MLPSGP-SAEA. First, the MLP grouping selects a subspace from the original space by evaluating the impact of each decision variable on the objective functions. Then, for each objective function, a sparse GP model is employed, and the locations of pseudo-input points are optimized to enhance computational efficiency while improving model accuracy. Moreover, an adaptive sparse and diverse (ASD) infill criterion is proposed, based on the characteristics of the sparse GP model predictive distribution, to better balance exploration and exploitation. Finally, extensive experiments are conducted on four benchmark suites and an aerodynamic design optimization problem. The experimental results demonstrate that MLPSGP-SAEA exhibits significant competitive advantages over the state-of-the-art SAEAs.

Combining cloud radio access network (C-RAN) and mobile edge computing (MEC) can effectively reduce network service latency and improve network reliability. The locations of edge servers (ESs) in the architecture and the services deployed affect the network quality and the operator’s revenue. However, few studies have focused on ES or service deployment in this architecture, and almost no research has addressed both joint deployments. Furthermore, previous studies rarely consider the collaborative deployment across multiple communities (regions) and the cooperation among ESs simultaneously. To fill this gap, this study first constructs a C-RAN network system model with multilayer MEC involving multicommunities and establishes a multiobjective mixed-integer programming (MOMIP) model aimed at maximizing the profit of service providers and the comprehensive average resource utilization of the ESs. Then, for the established model, this study proposes a three-step heuristic algorithm to solve. The algorithm has been used to determine the number of ESs to be deployed in each community, the deployment locations of ESs at each layer, and the services to be deployed on each ES. Finally, this study conducts a series of simulation experiments. Experimental results show that the proposed algorithm outperforms the baseline algorithms, and the obtained deployment scheme is more reasonable.

The combination of Cloud Radio Access Network (C-RAN) and Mobile Edge Computing (MEC) has been proven to effectively enhance network transmission rates and service capabilities. However, since the ground communication facilities in the architecture are fixed, have limited coverage and are easily damaged by natural disasters, some user requests may not be processed in a timely manner. This poses a significant challenge to the flexibility and post-disaster recovery capabilities of the architecture. To this end, this paper studies how to deploy Unmanned Aerial Vehicles (UAVs) to assist the recovery of post-disaster communication network when some ground facilities in the architecture are destroyed, and how to perform reasonable task scheduling based on user task priorities to achieve rapid and effective rescue. Moreover, this paper mathematically models the problem with the goal of maximizing the success rate of user task execution. Since the established model belongs to the Mixed-integer Nonlinear Programming (MINLP) model, which is non-convex and NP-Hard, this paper designs a priority-based algorithm for joint UAV deployment and task scheduling to obtain high-quality suboptimal solutions. The algorithm employs a two-layer optimization architecture with the characteristics of low memory usage and low time complexity. The experimental results indicate that the proposed algorithm outperforms the baseline algorithms and is more effective in handling rescue tasks under different user scales.

Resource-constrained devices in open environments face diverse optimization problems, so general-purpose optimization capabilities become important but are currently lacking. Our algorithm framework aims to fill this gap and better understand the issues when implementing general optimization in hardware, especially using field-programmable gate array. Therefore, the challenge is to design an algorithm framework that can handle different optimization problems with fewer hardware resources while maximizing solution performance. Based on the Zynq XC7Z020-2CLG400I device and the compact differential evolution (cDE) algorithm, this article describes the unified software and hardware architecture, the cDE algorithm that incorporates ensemble mutation and crossover strategy as well as uniform mutation operation (cDE-emc-um), providing an efficient and low-resource algorithm framework while maintaining generality. This article also theoretically analyzes the global convergence and low computational complexity of the cDE-emc-um and tests the general optimization capabilities of our algorithm framework through two optimization problems.

With the continuous development of the communication service industry, users pay more attention to the quality of network service. Previous studies on offloading problems, especially in the Cloud Radio Access Network (C-RAN) architecture with Mobile Edge Computing (MEC), are primarily focused on the economic perspective, with little consideration given to user-oriented satisfaction problems. To fill this gap, this article proposes a mathematical model for maximizing user satisfaction in the C-RAN architecture with multi-layer MEC. The problem is divided into two stages for solution. The first stage addresses the optimal connection problem between users and Remote Radio Heads (RRHs). The second stage then schedules user tasks reasonably based on the solution obtained in the first stage. The two-stage problems are all proved to be NP-Hard. Two efficient approximation algorithms, namely User-to-RRH Association Algorithm (URAA) and Maximum Satisfaction Algorithm (MSA), are proposed to solve the problems in different stages. This article proves and analyzes the theoretical performance of the two algorithms. Finally, the performance of the proposed algorithms is verified by simulation experiments. The experimental results demonstrate that the two proposed algorithms can achieve reasonable solutions to the problems, and the user satisfaction level can be maintained at a high level.

Meta-heuristic algorithms have been widely used in deep learning. A hybrid algorithm EO-GWO is proposed to train the parameters of long short-term memory (LSTM), which greatly balances the abilities of exploration and exploitation. It utilizes the grey wolf optimizer (GWO) to further search the optimal solutions acquired by equilibrium optimizer (EO) and does not add extra evaluation of objective function. The short-term prediction of traffic flow has the characteristics of high non-linearity and uncertainty and has a strong correlation with time. This paper adopts the structure of LSTM and EO-GWO to implement the prediction, and the hyper parameters of the LSTM are optimized by EO-GWO to transcend the problems of backpropagation. Experiments show that the algorithm has achieved wonderful results in the accuracy and computation time of the three prediction models in the highway intersection.

The construction and governance of smart cities require the collaboration of different systems and different regions. How to realize the monitoring of abnormal hot spots through the collaboration of subsystems with limited resources is related to the stability and efficiency of the city. This work constructs a hot data processing framework for drones and 5G edge computing infrastructure, as well as an Ensemble Multi-Objective Cooperative Learning method to process three different types of hot data. The data collection phase combines set operations with the 0-1 multi-knapsack model, and the cooperative learning phase realizes the degree of cooperation control while retaining the ability of independent optimization of the subsystem. Finally, the advantages of the framework are verified by hot data coverage and collaborative processing efficiency, resource use cost, and balance.

The wide application of wireless sensor networks (WSN) brings challenges to the maintenance of their security, integrity, and confidentiality. As an important active defense technology, intrusion detection plays an effective defense line for WSN. In view of the uniqueness of WSN, it is necessary to balance the tradeoff between reliable data transmission and limited sensor energy, as well as the conflict between the detection effect and the lack of network resources. This paper proposes a lightweight Intelligent Intrusion Detection Model for WSN. Combining k-nearest neighbor algorithm (kNN) and sine cosine algorithm (SCA) can significantly improve the classification accuracy and greatly reduce the false alarm rate, thereby intelligently detecting a variety of attacks including unknown attacks. In order to control the complexity of the model, the compact mechanism is applied to SCA (CSCA) to save the calculation time and space, and the polymorphic mutation (PM) strategy is used to compensate for the loss of optimization accuracy. The proposed PM-CSCA algorithm performs well in the benchmark functions test. In the simulation test based on NSL-KDD and UNSW-NB15 data sets, the designed intrusion detection algorithm achieved satisfactory results. In addition, the model can be deployed in an architecture based on cloud computing and fog computing to further improve the real-time, energy-saving, and efficiency of intrusion detection.

The wireless sensor network is a network composed of sensor nodes self‐organizing through the application of wireless communication technology. The application of wireless sensor networks (WSNs) requires high security, but the transmission of sensitive data may be exposed to the adversary. Therefore, to guarantee the security of information transmission, researchers propose numerous security authentication protocols. Recently, Wu et al. proposed a new three‐factor authentication protocol for WSNs. However, we find that their protocol cannot resist key compromise impersonation attacks and known session‐specific temporary information attacks. Meanwhile, it also violates perfect forward secrecy and anonymity. To overcome the proposed attacks, this paper proposes an enhanced protocol in which the security is verified by the formal analysis and informal analysis, Burross‐Abadii‐Needham (BAN) logic, and ProVerif tools. The comparison of security and performance proves that our protocol has higher security and lower computational overhead.

The key to the problem of node coverage in wireless sensor networks (WSN) is to deploy a limited number of sensors to achieve maximum coverage. This paper studies the hybrid strategies of multiple evolutionary algorithms, and applies them to the problem of WSN node coverage. We first proposed the hybrid algorithm SFLA‐WOA (SWOA) based on Shuffled Frog Leaping Algorithm (SFLA) and Whale Optimization Algorithm (WOA). The SWOA algorithm combines the advantages of SFLA and WOA; that is, it retains the unique evolution model of WOA and also has the excellent co‐evolution capability of SFLA. Secondly, using the mutation, crossover and selection operations of the differential evolution (DE) algorithm to further optimize this hybrid algorithm, the SWOA‐based SFLA‐WOA‐DE (SWOAD) algorithm is proposed. In addition, the performance of SWOA and SWOAD has been tested by 30 benchmark functions in the CEC 2017 test set. Experimental results show that the optimization effects of these two algorithms are very outstanding. Finally, the simulation results show that the optimization algorithm proposed in this paper has a good effect on improving the signal coverage of WSN under the actual three‐dimensional terrain.

Social networks are becoming popular, with people sharing information with their friends on social networking sites. On many of these sites, shared information can be read by all of the friends; however, not all information is suitable for mass distribution and access. Although people can form communities on some sites, this feature is not yet available on all sites. Additionally, it is inconvenient to set receivers for a message when the target community is large. One characteristic of social networks is that people who know each other tend to form densely connected clusters, and connections between clusters are relatively rare. Based on this feature, community-finding algorithms have been proposed to detect communities on social networks. However, it is difficult to apply community-finding algorithms to distributed social networks. In this paper, we propose a distributed privacy control protocol for distributed social networks. By selecting only a small portion of people from a community, our protocol can transmit information to the target community.

Wireless sensor networks (WSN) have gradually integrated into the concept of the Internet of Things (IoT) and become one of the key technologies. This paper studies the optimization algorithm in the field of artificial intelligence (AI) and effectively solves the problem of node location in WSN. Specifically, we propose a hybrid algorithm WOA-QT based on the whale optimization (WOA) and the quasi-affine transformation evolutionary (QUATRE) algorithm. It skillfully combines the strengths of the two algorithms, not only retaining the WOA’s distinctive framework advantages but also having QUATRE’s excellent coevolution ability. In order to further save optimization time, an auxiliary strategy for dynamically shrinking the search space (DSS) is introduced in the algorithm. To ensure the fairness of the evaluation, this paper selects 30 different types of benchmark functions and conducts experiments from multiple angles. The experiment results demonstrate that the optimization quality and efficiency of WOA-QT are very prominent. We use the proposed algorithm to optimize the weighted centroid location (WCL) algorithm based on received signal strength indication (RSSI) and obtain satisfactory positioning accuracy. This reflects the high value of the algorithm in practical applications.

Differential Evolution (DE) is a very simple but powerful Evolutionary Algorithm (EA) for real-world applications. There are two aspects affecting the overall optimization performance significantly, one is parameter control scheme and the other is trial vector generation strategy. As all the control parameters are employed in generating trial vectors, from this perspective of view, the trial vector generation strategy dominates the overall optimization performance. Therefore, trial vector generation strategies are often designed first, and then the corresponding parameter control schemes are well-tuned thereafter. Both of them constitute the main body of a new DE variant. Here in the paper, a novel DE variant with a new designed trial vector generation strategy as well as novel parameter control scheme is proposed to tackle freight trains scheduling problem. By incorporating both a time-stamp mechanism of the external archive and a novel parameter control scheme, the new algorithm can secure better optimization performance not only on man-made benchmarks but also on real-world applications. Both the man-made benchmarks from Congress on Evolutionary Computation (CEC) test suite and the real-world freight train scheduling problem are employed in the validation of the new DE variant. The experiment results show that it is competitive with other state-of-the-art DE techniques for these optimization problems.

An Echo-Aided Bat Algorithm (EABA) based on measurable movement is proposed to improve optimization efficiency in this study. The conception is to employ the echo time to measure the distance from bats and objective. The bats emit an ultrasound to objective to measure the time of a round trip between their position and objective position. The echo time can guide the bats to correct velocity, direction and movement step. And the bats can more accurately measure the position of objective to adjust its step to find the better solution. There are many scenarios with different population sizes and objective functions to verify the performance of the proposed EABA. The experimental numeric result shows that EABA has better ability of search to improve the quality of the best solution than BA. The solution performance is improved by 45% and 30% for the functions of low complexity and high complexity in comparison with the original bat algorithm, respectively.

In this study, we propose an ACS with flip-flop search strategy to find the route from source node to destination node in an Ad hoc network topology. A flip-flop search strategy is to alternate the search direction towards either high pheromone area or low pheromone area iteratively in the evolution process. The proposed Flip-Flop search strategy effectively solves the pheromone-excess problem in ACS. The ants are allowed to select reverse path to avoid the ants affected by the high pheromone concentration and disable the ability of discover new search area in routing phase. In simulations, the proposed Flip-Flop Ant Colony System (FFACS) is compared with Traditional Ant Colony System in conditions of various deployment densities and topologies of wireless sensor network. The results show that the FFACS has promising ability of discover new search area to reach the better optimal solution than the TACS has. In addition, the robustness and the stability of FFACS are better than TACS.

A novel feature extraction method, namely Laplacian discriminant projection with optimized kernels (KLDP-Opt) algorithm is proposed in this paper. The advantage of KLDP-Opt lies in: 1) the similarity matrix is constructed with the class-wise nonparametric similarity measure where it solves procedure selection problem; 2) data-dependent kernel is applied to solve the limitation of linearity of LPP, where the adaptive parameters of the data-dependent kernel are computed through optimizing an objective function designed for measuring the class separability of data in the feature space. Besides the theory derivation, the experiments are implemented on ORL and Yale face databases to evaluate the feasibility of the proposed algorithm.

The work presents a novel, efficient, nearest-neighbor codeword search algorithm based on three elimination criteria in the Hadamard transform (HT) domain. Before the search process, all codewords in the codebook are Hadamard-transformed and sorted in the ascending order of their first elements. During the search process, we first perform the HT on the input vector and calculate its variance and norm, and secondly exploit three efficient elimination criteria to find the nearest codeword to the input vector using the up-down search mechanism near the initial best-match codeword. Experimental results demonstrate that the performance of the proposed algorithm is much better than that of most existing nearest neighbor codeword search algorithms, especially in the case of high dimension.

Codeword Index Assignment (CIA) is a key issue to vector quantization (VQ). A new algorithm called Modified Tabu Search Algorithm (MTSA) is applied to codeword index assignment for noisy channels for the purpose of minimizing the distortion due to bit errors. Simulated annealing (SA) technique and a new parameter are introduced in the Tabu Search Approach (TSA) to improve the performance of the tabu search approach. Experimental tests show the modified tabu search algorithm is superior to the tabu search algorithm by evaluating the performance of channel distortion after the same number of iterations.

ABSTRACT Vector quantization is a popular technique in low bit rate codingof speech signal. The transmission index of the codevector ishighly sensitive to channel noise. The channel distortion canbe reduced by organizing the codevector indices suitably.Several index assignment algorithms are studied comparatively.Among them, the index allocation algorithm proposed by Wuand Barba is the fastest method but the channel distortion is theworst one. The proposed parallel tabu search algorithm reachthe best performance of channel distortion. 1.INTRODUCTION Vector quantization (VQ) [1] is a widely used technique for datacompression. The binary indices of the optimally chosencodevectors are sent to the destination. A vectorXxx x={, , , } 12  k consisting of k samples of informationsource in the k-dimensional Euclidean space R k is sent to thevector quantizer. The k-dimensional vector quantizer with thenumber of codevectors N is defined as follows by using thereproduction alphabet consisting of N codevectors,Ccc c={,,, }