The Industrial Internet of Things (IIoT) enhances production efficiency through inter-factory resource sharing and intelligent human-machine interaction (HMI) in flexible manufacturing. This study addresses large-scale multiobjective resource scheduling optimization in the complex IIoT environments to improve production efficiency and resource scheduling under growing customization and order demands. We develop a multiobjective IIoT scheduling model under assumptions of task priority and resource constraints, optimizing makespan, logistics time, energy consumption, tardiness time, production cost, and carbon emission. To improve resource scheduling performance, a fuzzy decision-enhanced dual directed sampling-assisted large-scale multiobjective optimization algorithm (LMOEA-FDDS) is proposed. The algorithm employs a dual directed sampling method, uses two different types of search directions to guide the population evolution, and combines a complementary environmental selection strategy based on angle penalty distance (APD), effectively improving the algorithms search eiciency and performance. Experimental results show that LMOEA-FDDS achieves 30%-70% higher HV values than state-of-the-art algorithms, demonstrating its effectiveness in solving complex IIoT scheduling problems.

Neural Architecture Search (NAS) requires global topological exploration and is hence time consuming. To address this challenge, we propose the comprehensive-forecast multiobjective genetic programming for NAS, or CFMOGP-NAS for short. By integrating the strengths of various regression models and synthesizing the forecast of multiple candidates, the accuracy and robustness of architecture predictions are enhanced. The resultant algorithm this way incorporates a strategy of a mixture of complete and partial training, which balances cost and accuracy of evaluation. To also balance the population diversity, we develop a regularized tournament scheme for genetic programming. Experimental studies show that CFMOGP-NAS achieves a 50% reduction in search time without sacrificing accuracy, and verify that it substantially improves efficacy and efficiency compared with the state-of-the-art NAS methods.

Vehicle edge computing (VEC) can effectively meet the demand for computing resources in autonomous driving. However, complex resource constraints exist in the practical application of VEC, making offloading tasks a key challenge. Traditional scheduling algorithms are usually optimized only for latency and cost and can handle only a small number of tasks; however, they cannot handle real-world intensive vehicle-road cooperation scenarios involving many tasks. Thus, this article constructs a large-scale multiobjective computing offloading optimization model that comprehensively considers latency, energy consumption, load balancing, and resource utilization. To improve the offloading performance of VEC, we propose a large-scale multiobjective optimization algorithm with hybrid directed sampling and adaptive offspring generation (LMOEA-HDGS). The algorithm can generate adaptive offspring by sampling in two types of search directions in the decision space and can adapt to the complex shape of the Pareto front while balancing diversity and convergence. The experimental results show that the proposed algorithm can effectively optimize the task offloading problem of VEC in an intensive vehicle-road cooperation scenario.

This article proposes a cloud-edge–terminal collaborative resource allocation architecture that efficiently allocates resources. Traditional resource allocation often focuses solely on optimizing delay and service cost, making it less suitable for intensive real-world scenarios. In response, a comprehensive multiobjective resource allocation model is developed, encompassing delay, service cost, load balancing, and resource utilization. This article proposes a diversity-filling large-scale multiobjective evolutionary algorithm based on generative adversarial networks (DFGAN-LSMOEA). The Otsu-based grouping method in DFGAN-LSMOEA is employed to group decision variables and improve optimization performance. Compared with state-of-the-art algorithms, the proposed method validates its effectiveness and advantages in the applications, particularly when handling high-dimensional decision variables and dynamic demands.

In today’s increasingly complex and dynamic cyber threat environment, Zero Trust Architecture (ZTA) has emerged as a promising solution to address the limitations of traditional intrusion detection methods. While Intrusion Detection Systems (IDS) are essential for safeguarding organizational information assets, traditional methods have the risk of exposing security policies by collecting and utilizing alarm data, potentially revealing attack paths to adversaries. To overcome this challenge, we propose a novel intrusion detection strategy based on ZTA, emphasizing the protection of alarm data privacy. Additionally, we introduce an adaptive sparse connective evolutionary neural architecture search (ASCE-NAS) framework, which enables the automatic evolution of intrusion detection model structures to enhance adaptability and performance in dynamic environment. Notably, ASCE-NAS can also be beneficial for integrated sensing and computing chips and systems, contributing to a more secure and efficient cybersecurity framework to effectively combat evolving threats and attack methods.

Deep learning has made remarkable achievements in many fields. However, although fuzzy neural networks with natural interpretability are widely used in prediction and control scenarios, there are very few studies on the deepening of fuzzy systems. By integrating rough set theory, fuzzy rough neural network has unique advantages benefiting from the complementarity of the fuzzy set theory and the rough set theory as well as the powerful learning ability of neural networks. Similarly, research on deep fuzzy rough neural networks is even rarer. In this article, in order to improve the performance of the fuzzy rough neural network and expand its application range, the deep fuzzy rough neural network model is constructed and optimized by stacking blocks of fuzzy rough neural network to imitate the deepening deep neural network based on multiobjective evolution. Each fuzzy rough neural network block is interpretable, and its stacked architecture also has high interpretability. To automatically generate deep fuzzy rough neural network models with high efficiency, a distributed parallel multiobjective neuroevolution framework is developed, thus blocks can be stacked flexibly and deep architecture can be optimized considering multiple optimization objectives of accuracy, interpretability, and generalization simultaneously. In addition, multiobjective evolution is combined with Wang–Mendel method, pseudoinverse, and backpropagation to effectively learn specific parameters. Finally, based on the time series prediction problems, the superiority of the multiobjective deep fuzzy rough neural network evolutionary framework is verified.

Deep neural networks can provide environment sensing and decision support for vehicles in 6G intelligent autonomous transportation systems; however, the high computational cost associated with complex tasks limits the deployment of models on edge devices. To address this issue, this paper introduces a large-scale multiobjective filter pruning method, which stratifies the population by Angle Penalty Distance (APD) and selects the individuals to be updated. Meanwhile, the global search capability of the algorithm is enhanced by combining sampling update strategies and quantum behavioral update, in different situations. Moreover, a dynamic optimization tuning strategy is proposed to regulate the balance between exploration and exploitation within the algorithm. In the depth estimation task, the model is pruned using three objectives: root mean square error (RMSE), number of parameters, and FLOPs. Experimental results indicate that the pruned model obtains the minimum RMSE and the maximum compression ratio of the number of parameters and FLOPs, and can be effectively deployed in sensing-computing integrated chip and system for intelligent transportation systems.

The rapid expansion of intelligent vehicles in 6G networks has intensified the demand for real-time task processing. However, traditional cloud-edge collaboration models for large-scale vehicle task offloading are increasingly inadequate to address the growing complexity and demands. To address this challenge, we propose a unified cloud-edge-end collaborative vehicle task offloading multiobjective optimization model for large-scale vehicle task offloading, which simultaneously considers four optimization objectives: latency, energy consumption, load balancing and quality of service (QoS). To solve the large-scale multiobjective optimization problem, we propose a large-scale multiobjective evolutionary algorithm based on problem transformation and bidirectional vectors (LSMOEA-PTBV). Experiments in a simulated 6G vehicular network demonstrate that LSMOEA-PTBV outperforms state-of-the-art methods. Our work enhances the end-user experience, meets the increasingly complex demands of modern applications, and advances the development of integrated sensing and computing systems and intelligent transportation systems in the 6G era.

While Digital Twins (DTs) bring convenience to city managers, they also generate new challenges to city network security. Currently, cyberspace security becomes increasingly complicated. Intrusion detection and Deep Learning (DL) are combined with shunning security threats in service computing systems and improving network defense capabilities. DTs can be applied to network security. People's understanding of cyberspace security can be improved using DTs to digitally define, model, and display the network environment and security status. The intrusion detection data are optimized based on DL technology, and a network intrusion detection algorithm integrated with Deep Neural Network (DNN) model is proposed. In the cloud service system, a trust model based on Keyed-Hashing-based Self-Synchronization (KHSS) is introduced. This model predicts the security state and detects attacks according to existing malicious attacks, ensuring the network security defense system's regular operation. Finally, simulation experiments verify the Deep Belief Networks (DBN) model's feasibility and the cloud trust model. The DBN algorithm proposed improves the correct detection rate of unknown samples by 4.05% compared with the Support Vector Machine (SVM) algorithm. From the 20,100 pieces of data in the test dataset, the number of correct attacks detected by the DBN algorithm exceeds those by the SVM algorithm by 818. DBN algorithm requires a short detection time while ensuring optimal detection accuracy. The KHSS+DBN model predicts cloud security states, and the results are the same as the actual states, with an error of only 1%∼2%.

In vehicular edge computing (VEC), vehicle users (VUs) can offload their computation-intensive tasks to edge server (ES) that provides additional computation resources. Due to the edge server being closer to VUs, the propagation delay between the ESs and the VUs is lower compared to cloud computing. Applying digital twin to VEC allows for low-cost trial in task offloading. In real-word, the mobility of VUs cannot be ignored and the downlink delay in receiving process results from ES is related to the mobility of VUs. Therefore, a five-objective optimization model including downlink delay, computation delay, energy consumption, load balancing, and user satisfaction of the VUs is constructed. To solve the above model, an improved CMA-ES algorithm based on the guiding point (GP-CMA-ES) is proposed. When the number of VUs increases, the dimension of variables also increases. Therefore, a convergence-related variable grouping strategy based on the relationship detection between variables and objectives is proposed. The performance of algorithm GP-CMA-ES is compared with five algorithms in the digital twin environment.

The wide area measurement system (WAMS) based on synchronous phasor measurement technology plays an increasingly important role in dynamic monitoring and wide area protection of modern power systems. If the phasor measurement unit (PMU) is placed on all buses of the power system, the voltage and branch current of all buses can be directly observed. However, due to the high placement cost of PMU and its ability to measure the voltage phasor of the installed bus and the current of the associated branch, it is unrealistic and unnecessary to install PMU on all buses of the system. This article discusses the incomplete observability under single PMU loss (N-1) contingencies and its effect on PMUs placement. An improved two-archive algorithm is proposed to solve the five-objective placement optimization model. In addition, a fuzzy decision-making method combining subjective and objective is proposed to help power grid enterprises select the most appropriate solution. The proposed method is tested on several IEEE bus systems and Polish 2383-bus system, and the test results verify its effectiveness.

The space–air–ground-integrated network (SAGIN) can enhance the performance of the Internet of Vehicles (IoV). However, the basic hardware differences among communication systems are large, which leads to communication difficulties between different communication systems. To effectively manage multiple communication networks (satellite networks, air networks, and terrestrial networks) and computing resources in IoV, this article proposes a SAGIN-IoV edge–cloud architecture based on software-defined networking (SDN) and network function virtualization (NFV). In addition, we construct an optimization model based on SAGIN-IoV’s service requirements, and propose an improved algorithm. Experimental results show that the improved algorithm can effectively optimize the resource scheduling problem of SAGIN-IoV.

Recommender systems are of great significance for mining the data generated by the Internet of Things (IoT) and are important for the intelligent IoT systems. The traditional recommendation algorithms only consider the accuracy as the optimization objective. In this article, a many-objective optimization model consisting of the F1 measure, recommendation novelty, recommendation coverage, customer satisfaction, landmark similarity, and overfitting is constructed for recommendation. Then, to improve the recommendation performance, we propose to use a large-scale many-objective optimization algorithm based on problem transformation (LSMaOA) to optimize the matrix factorization model for the recommender system in the intelligent IoT systems. The experimental results show that LSMaOA is robust and can effectively optimize the model’s six objectives. Compared with the knee point-driven evolutionary algorithm (KnEA), the grid-based evolutionary algorithm (GrEA), the large-scale multiobjective optimization framework (LSMOF), and the reference vector guided evolutionary algorithm (RVEA), the proposed algorithm can promote the F1 measure by 7.78%, 13.63%, 21.85%, and 28.63%, respectively.

The fuzzy logic-based neural network usually forms fuzzy rules via multiplying the input membership degrees, which lacks expressiveness and flexibility. In this article, a novel neural network model is designed by integrating the gene expression programming into the interval type-2 fuzzy rough neural network, aiming to generate fuzzy rules with more expressiveness utilizing various logical operators. The network training is regarded as a multiobjective optimization problem through simultaneously considering network precision, explainability, and generalization. Specifically, the network complexity can be minimized to generate concise and few fuzzy rules for improving the network explainability. Inspired by the extreme learning machine and the broad learning system, an enhanced distributed parallel multiobjective evolutionary algorithm is proposed. This evolutionary algorithm can flexibly explore the forms of fuzzy rules, and the weight refinement of the final layer can significantly improve precision and convergence by solving the pseudoinverse. Experimental results show that the proposed multiobjective evolutionary network framework is superior in both effectiveness and explainability.

The sensor nodes in wireless sensor networks have the deficiency of limited energy, and the multihop transmission of information will lead to a premature paralysis of nodes near the sink. The use of the mobile sink can balance the energy consumption and greatly prolong the lifetime. Therefore, this article studies the scheduling strategy of multiple mobile sinks and proposes a heuristic strategy based on interval type-2 fuzzy rough neural network. The energy and lifetime of sensor nodes, as well as location information of the mobile sink and special nodes are taken as input features. Through neural network learning, the outputs determine whether to move, moving direction, moving distance, and residence time, which can complete the scheduling task. The scheduling problem is regarded as a multiobjective optimization problem, and the network lifetime, the moving path length, and the network interpretability are optimized at the same time, so as to obtain a lightweight network with good interpretability and performance. Based on the parallel multiobjective evolutionary algorithm, a multiobjective neural evolutionary framework is constructed. This framework can balance multiple objectives and complete complex scheduling tasks. Compared with static sinks, random-moving sinks, sinks with manually designed strategy, gene expression programming-based sinks, as well as the other state-of-the-art multiobjective evolutionary algorithms, the proposed framework can achieve superior results.

Medical data widely exist in the hospital and personal life, usually across institutions and regions. They have essential diagnostic value and therapeutic significance. The disclosure of patient information causes people’s panic, therefore, medical data security solution is very crucial for intelligent health care. The emergence of federated learning (FL) provides an effective solution, which only transmits model parameters, breaking through the bottleneck of medical data sharing, protecting data security, and avoiding economic losses. Meanwhile, the neural architecture search (NAS) has become a popular method to automatically search the optimal neural architecture for solving complex practical problems. However, few papers have combined the FL and NAS for simultaneous privacy protection and model architecture selection. Convolutional neural network (CNN) has outstanding performance in the image recognition field. Combining CNN and fuzzy rough sets can effectively improve the interpretability of deep neural networks. This article aims to develop a multiobjective convolutional interval type-2 fuzzy rough FL model based on NAS (CIT2FR-FL-NAS) for medical data security with an improved multiobjective evolutionary algorithm. We test the proposed framework on the LC25000 lung and colon histopathological image dataset. Experimental verification demonstrates that the designed multiobjective CIT2FR-FL-NAS framework can achieve high accuracy superior to state-of-the-art models and reduce network complexity under the condition of protecting medical data security.

The deployment of a very large number of readers in a limited space may increase the probability of collision among radio-frequency identification (RFID) readers and reduce the dependability and controllability of Internet-of-Things (IoT) systems. Intelligent computing technologies can be used to realize intelligent management by scheduling resources to circumvent collision issues. In this article, an improved RFID reader anticollision model is constructed by modifying the measure index, introducing a constraint function, and simultaneously considering collisions among readers and between readers and tags. The dense deployment of large numbers of readers increases the number of variables to be encoded, resulting in a high-dimensional problem that cannot be effectively and efficiently solved by traditional algorithms. Accordingly, distributed parallel cooperative co-evolution particle swarm optimization (DPCCPSO) is proposed. The inertia weight and learning factors are adjusted during evolution, and an improved grouping strategy is presented. Moreover, various combinations of random number generation functions are tested. For improved efficiency, DPCCPSO is implemented with distributed parallelism. Experimental verification shows that the proposed novel algorithm exhibits superior performance to existing state-of-the-art algorithms, particularly when numerous RFID readers are deployed.

With the advent of the Internet of Things, especially the Internet of Vehicles, abundant environmental and mobile data can be generated continuously. A personalized recommender system is one of the important methods for solving the problem of big data overload. However, to make use of these mobile data from vehicles, traditional recommender services are confronted by severe challenges. Therefore, we study the diversified recommendation problem based on a real-world dataset, represented as a tensor with three dimensions of user, location and activity. As the tensor is rather sparse, we employ tensor decomposition to predict missing values. Additionally, we directly regard recommendation precision as an objective. In addition to precision, we also consider the recommendation novelty and coverage, providing a more comprehensive view of the recommender system. Thus, visitors can discover attractive spots that are less visited in a personalized manner, relieving traffic pressure at famous scenic spots and balancing overall transportation. By integrating all these objectives, we construct a many-objective recommendation model. To optimize this model, we propose a distributed parallel evolutionary algorithm employing the nondominated ranking and crowding distance. Compared with the state-of-the-art algorithms, the proposed algorithm performs well and is very efficient.

The development of 5G technology has brought the information revolution of the Internet of Things (IoT). With the emergence of intelligent products, such as the Internet of connected vehicles (IoCV) and wireless sensor nodes, the classification and disposal of municipal waste is now more intelligent and efficient. How to arrange waste collection vehicles reasonably and optimize their service routes in real time based on signals sent by intelligent waste bins to meet the daily needs of residents has become an urgent problem. An improved multiobjective model for the split-delivery vehicle routing problem (SDVRP) is proposed based on the multiple cleaning services for the same collection point in a certain period of time according to the real-time waste volume. The process of classified waste disposal in the Tianjin Wudadao area is analyzed as an example. The experimental results show that the Pareto-optimal solutions obtained by the algorithm can effectively arrange the vehicle service routes, meet the needs of classified waste collection in the area, and provide a scientific theoretical basis for the further development of waste classification.

The development of the Internet of Vehicles (IoV) has made transportation systems into intelligent networks. However, with the increase in vehicles, an increasing number of data need to be analyzed and processed. Roadside units (RSUs) can offload the data collected from vehicles to remote cloud servers for processing, but they cause significant network latency and are unfriendly to applications that require real-time information. Edge computing (EC) brings low service latency to users. There are many studies on computing offloading strategies for vehicles or other mobile devices to edge servers (ESs), and the deployment of ESs cannot be ignored. In this paper, the placement problem of ESs in the IoV is studied, and the six-objective ES deployment optimization model is constructed by simultaneously considering transmission delay, workload balancing, energy consumption, deployment costs, network reliability, and ES quantity. In addition, the deployment problem of ESs is optimized by a many-objective evolutionary algorithm. By comparing with the state-of-the-art methods, the effectiveness of the algorithm and model is verified.

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 efficiency of hybrid microgrid systems (HMSs) are often in conflict; hence, a reasonable design for the sizing of the initial microgrid is important. In this article, we propose an improved two-archive many-objective evolutionary algorithm (TA-MaEA) based on fuzzy decision to solve the sizing optimization problem for HMSs. For the HMS simulated in this article, costs, loss of power supply probability, pollutant emissions, and power balance are considered as objective functions. For the proposed algorithm, we employ two archives with different diversity selection strategies to balance convergence and diversity in the high-dimensional objective space. In addition, a fuzzy decision making method is proposed to further help decision makers obtain a solution from the Pareto front that optimally balances the objectives. The effectiveness of the proposed algorithm in solving the HMS sizing optimization problem is investigated for the case of Yanbu, Saudi Arabia. The experimental results show that, compared with the two-archive evolutionary algorithm for constrained many-objective optimization (C-TAEA), the clustering-based adaptive many-objective evolutionary algorithm (CA-MOEA), and the improved decomposition-based evolutionary algorithm (I-DBEA), the proposed algorithm can reduce the system costs by 7%, 13%, and 21%, respectively.

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. In this article, 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 the 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 the existing fuzzy rough neural network and the long short-term memory network.

Security is crucial for industrial wireless sensor networks (IWSNs); therefore, in this article, 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.

The traditional deployment research on wireless sensor networks (WSNs) has mainly focused on 2D plane and 3D full space; also, the sensors considered are almost always omni-directional sensors and are usually homogeneous. However, this type of research cannot fulfill the diverse requirements required for practical 3D environment. We study the deployment problem of heterogeneous directional sensor networks (HDSNs) on 3D terrain, which is more suitable for practical security monitoring requirements and has more practical significance. In this paper, we propose a novel uncertain comprehensive coverage model: a modified 3D directional sensing model is presented, and a non-probabilistic measure based fusion operator is utilized. We transform the deployment problem into a multiobjective optimization problem by comprehensively considering Coverage, Connectivity Uniformity and Deployment Cost, and we use various state-of-the-art multiobjective optimization algorithm based deployment approaches to address it. We conduct deployment experiments on three types of real-world 3D terrain data (plain, hill and mountain). Through the analysis of the deployment results, we obtain a deeper understanding and insight into the multiobjective deployment problem of HDSN. Meanwhile, we more clearly recognize the characteristics of the deployment approaches.

The development of smart cities and the emergence of three-dimensional (3-D) urban terrain data have introduced new requirements and issues to the research on the 3-D deployment of wireless sensor networks. We study the deployment issue of heterogeneous wireless directional sensor networks in 3-D smart cities. Traditionally, studies on the deployment problem of WSNs focus on omnidirectional sensors on a 2-D plane or in full 3-D space. Based on 3-D urban terrain data, we transform the deployment problem into a multiobjective optimization problem, in which objectives of Coverage, Connectivity Quality, and Lifetime, as well as the Connectivity and Reliability constraints, are simultaneously considered. A graph-based 3-D signal propagation model employing the line-of-sight concept is used to calculate the signal path loss. Novel distributed parallel multiobjective evolutionary algorithms (MOEAs) are also proposed. For verification, real-world and artificial urban terrains are utilized. In comparison with other state-of-the-art MOEAs, the novel algorithms could more effectively and more efficiently address the deployment problem in terms of optimization performance and operation time.

Wireless sensor networks (WSNs) are applied more and more widely in real life. In actual scenarios, 3-D directional wireless sensor nodes are constantly employed, thus, research on the real-time deployment optimization issue of 3-D directional WSNs based on terrain big data has more practical significance. Based on this, we study the deployment optimization issue of directional WSNs in the 3-D terrain through comprehensive consideration of coverage, lifetime, connectivity of sensor nodes, connectivity of cluster headers, and reliability of directional WSNs. We present a modified differential evolution algorithm by adopting crossover rate sort and polynomial-based mutation on the basis of the cooperative coevolutionary framework, and apply it to address the deployment problem of 3-D directional WSNs. In addition, to reduce computation time, we realize implementation of message passing interface parallelism. As is revealed by the experimentation results, the modified algorithm proposed in this paper achieves better performance with respect to either optimization results or operation time.

Effectively monitoring maritime environments has become a vital problem in maritime applications. Traditional methods are not only expensive and time consuming but also restricted in both time and space. More recently, the concept of an industrial wireless sensor network (IWSN) has become a promising alternative for monitoring next-generation intelligent maritime grids, because IWSNs are cost-effective and easy to deploy. This paper focuses on solving the issue of 3-D IWSN deployment in a 3-D engine room space of a very large crude-oil carrier and also considers numerous power facilities. To address this 3-D IWSN deployment problem for maritime applications, a 3-D uncertain coverage model is proposed that uses a modified 3-D sensing model and an uncertain fusion operator. The deployment problem is converted into a multiobjective optimization problem that simultaneously addresses three objectives: coverage, lifetime, and reliability. Our goal is to achieve extensive coverage, long network lifetime, and high reliability. We also propose a distributed parallel cooperative coevolutionary multiobjective large-scale evolutionary algorithm for maritime applications. We verify the effectiveness of this algorithm through experiments by comparing it with five state-of-the-art algorithms. Numerical results demonstrate that the proposed method performs most effectively both in optimization performance and in minimizing the computation time.

A considerable amount of research has been devoted to multiobjective optimization problems. However, few studies have aimed at multiobjective large-scale optimization problems (MOLSOPs). To address MOLSOPs, which may involve big data, this paper proposes a message passing interface MPI -based distributed parallel cooperative coevolutionary multiobjective evolutionary algorithm (DPCCMOEA). DPCCMOEA tackles MOLSOPs based on decomposition. First, based on a modified variable analysis method, we separate decision variables into several groups, each of which is optimized by a subpopulation (species). Then, the individuals in each subpopulation are further separated to several sets. DPCCMOEA is implemented with MPI distributed parallelism and a two-layer parallel structure is constructed. We examine the proposed algorithm using the multiobjective test suites Deb-Thiele-Laumanns-Zitzler and Walking-Fish-Group. In comparison with cooperative coevolutionary generalized differential evolution 3 and multiobjective evolutionary algorithm based on decision variable analyses, which are state-of-the-art cooperative coevolutionary multiobjective evolutionary algorithms, experimental results show that the novel algorithm has better performance in both optimization results and time consumption.

Traditional particle swarm optimization algorithms (PSO) targeted to solve large scale problems are mostly serial, such as CCPSO2, and the computing time is very long in general. Therefore, this paper presents a novel parallel PSO, which explores the usage of new probability distribution functions for the replacement of traditional Gaussian and Cauchy distributions, and the combination of GPSO and LPSO to make use of space exploration and speed up the convergence. As to the implementation of algorithm parallelization, we adopt the Spark platform, which is one of the currently most popular big data processing tools. We make modification to dynamic grouping and multiple calculations, in order to increase the degree of parallelism, reduce the computation time and improve algorithm efficiency as far as possible. Multiple computing refers to that in each single distribution of tasks, one computing node processes the particle position information of multiple algorithms. In the control of space exploration and convergence rate, we present a more efficient method to explore the solution space, which controls the convergence rate to enhance the exploration to a greater extent and also ensures fast convergence rate at the later stage, thus, it not only guarantees the calculation speed, but also improves the optimization effect as more as possible. We used twenty LSGO benchmark functions in CEC'2010 to make experiments, showing that the proposed algorithm could obtain satisfactory results, and for some functions, it outperforms DECC and MLCC.