In the traditional cloud-based Internet of Vehicles (IoV) architecture, it is difficult to guarantee the low latency requirements of the current intelligent transportation system (ITS). As a supplement to cloud computing, fog computing can effectively alleviate the bottlenecks of cloud computing bandwidth and computing resources and improve the quality of service (QoS) of the IoV. However, as a distributed system that operates near users, fog computing has a complicated network structure. In the complex and dynamic IoV environment, to effectively manage these computing resources with different attributes and provide high-quality services, it is necessary to design an efficient architecture and a resource allocation algorithm. Therefore, on the basis of fog-cloud computing and software-defined networking (SDN), a novel 5G IoV architecture is designed. In addition, after fully considering the service requirements of the IoV, a model of four objectives is constructed, and a many-objective optimization algorithm is proposed. The experiment results show that the proposed algorithm outperforms the other state-of-the-art algorithms.

The economics, reliability and carbon efficiencyof the hybrid system microgrid (HMS) often conflict; hence,reasonable design of the initial microgrid size is important. Wepropose an improved two-archive many-objective evolutionaryalgorithm (TAMaEA) based on fuzzy decisions to solve theoptimal sizing problem for an HMS. For the HMS, the costof power generation, loss of power supply probability, pollutantemissions, and power balance are considered objective functions.For the proposed algorithm, we employ two archives withdifferent diversity selection strategies to balance convergence anddiversity in the high-dimensional objective space. In addition, afuzzy decision method is proposed to further help decision makersobtain a solution from the Pareto front that optimally balanceseach objective. The effectiveness of the proposed algorithm insolving the HMS size optimization problem is investigated forthe case of Yanbu, Saudi Arabia. The simulation results provethat compared with the two-archive evolutionary algorithm forconstrained many-objective optimization (CTAEA), the improveddecomposition-based evolutionary algorithm (IDBEA) and theclustering-based adaptive many objective evolutionary algorithm(CAMOEA), the proposed algorithm can optimize the size ofthe system; the system cost is reduced by 7%, 13%, and 21%, respectively.

Air pollution has become an important environmental issue in recent decades. Forecasts of air quality play an important role in warning people about and controlling air pollution. We used support vector regression (SVR) and random forest regression (RFR) to build regression models for predicting the Air Quality Index (AQI) in Beijing and the nitrogen oxides (NO_X) concentration in an Italian city, based on two publicly available datasets. The root-mean-square error (RMSE), correlation coefficient (r), and coefficient of determination (R²) were used to evaluate the performance of the regression models. Experimental results showed that the SVR-based model performed better in the prediction of the AQI (RMSE = 7.666, R² = 0.9776, and r = 0.9887), and the RFR-based model performed better in the prediction of the NO_X concentration (RMSE = 83.6716, R² = 0.8401, and r = 0.9180). This work also illustrates that combining machine learning with air quality prediction is an efficient and convenient way to solve some related environment problems.

Fuzzy rough theory can describe real-world situations in a mathematically effective and interpretable way, while evolutionary neural networks can be utilized to solve complex problems. Combining them with these complementary capabilities may lead to evolutionary fuzzy rough neural network with the interpretability and prediction capability. We propose modifications to the existing models of fuzzy rough neural network and then develop a powerful evolutionary framework for fuzzy rough neural networks by inheriting the merits of both the aforementioned systems. We first introduce rough neurons and enhance the consequence nodes, and further integrate the interval type-2 fuzzy set into the existing fuzzy rough neural network model. Thus, several modified fuzzy rough neural network models are proposed. While simultaneously considering the objectives of Prediction Precision and Network Simplicity, each model is transformed into a multiobjective optimization problem by encoding the structure, membership functions and the parameters of the network. To solve these optimization problems, distributed parallel multiobjective evolutionary algorithms are proposed. We enhance optimization processes with several measures including optimizer replacement and parameter adaption. In the distributed parallel environment, the tedious and time-consuming neural network optimization can be alleviated by numerous computational resources, significantly reducing the computational time. Through experimental verification on complex stock time series prediction tasks, the proposed optimization algorithms and the modified fuzzy rough neural network models exhibit significant improvements over existing fuzzy neural networks and the long short-term memory network.

Security is crucial for industrial wireless sensor networks (IWSNs); therefore, in this paper, we simultaneously consider the security, lifetime and coverage issues by deploying sensor nodes and relay nodes in an industrial environment to analyze the multipath routing for enhancing security. For the security issue, the computation of disjoint routing paths is converted to a maximum flow problem. Then, the deployment problem is transformed into a multiobjective optimization problem, which we address by employing six state-of-the-art serial algorithms and two distributed parallel algorithms. Additionally, based on our prior work, by testing random grouping and prior knowledge-based grouping, as well as another optimizer, we propose enhanced distributed parallel algorithms. As verified by experiments, the proposed algorithms outperform their counterparts. Due to the characteristic of distributed parallelism, the time consumed by the proposed algorithms is significantly reduced compared to that of the serial algorithms. Therefore, the proposed algorithms can achieve better performance within a very limited time.

As one of the next-generation network technologies for data centers, wireless data center networks have important research significance. Smart architecture optimization and management are vital for wireless data center networks. With the ever-increasing demand for data center resources, the deployment of the data servers are on the rise. However, traditional wired links among servers are expensive and inflexible. Benefitting from the development of intelligent optimization and other techniques, this article studies a high-speed wireless topology for wireless data center networks. A radio propagation model based on a heat map is constructed. The line-of-sight issue and the interference problem are also discussed. By simultaneously considering the objectives of coverage, propagation intensity, and interference intensity, as well as the constraint of connectivity, the topology optimization problem is formulated as a multiobjective optimization problem. To seek the solutions, several state-of-the-art serial multiobjective evolutionary algorithms (MOEAs), as well as parallel MOEAs, are employed. Prior knowledge is preferred for the grouping, and parameter adaptation is conducted in the distributed parallel algorithms. Experimental results demonstrate that the parallel MOEAs perform effectively in the optimization results and efficiently in time consumption.