Forest fire monitoring plays an important role in forest resource protection. Although satellite remote sensing is an effective way for forest fire monitoring, satellite-based methods can only monitor huge scale forest areas, and they are weak in predicting the specific areas of forest fires. In this paper, we first propose an unmanned aerial vehicle (UAV)-enabled system architecture consisting of multiple industrial internet of things (IIoTs), in which the collected data by sensors in IIoTs can be delivered to UAVs for processing directly. As the sensors of IIoTs are deployed to monitor different indexes of forest fires, fully considering the priority constraints among sensors can guarantee a quick response of forest fire monitoring. Thus, the priority constraints among the sensors are taken into consideration in this system architecture, and the objective to minimize the maximum response time of forest fire monitoring. To search for the optimal UAV resource allocation strategy, a learning based cooperative particle swarm optimization algorithm (LCPSO) with Markov random field (MRF) based decomposition strategy is proposed. The solution space of UAV resource allocation is decomposed into sub solution space according to the decomposed decision variables by the MRF network structure, and the optimal resource allocation strategy is searched by LCPSO in multiple sub solution space cooperatively. Three simulation experiments on two datasets are designed, and the simulation results compared with the state-of-the-art methods verify the validity of LCPSO, which are reflected by the quickest response time of forest fire monitoring.

Flexible scheduling is one of the most significant core techniques for intelligent manufacturing systems. Realization of an optimized schedule through flexible resources assignment is critical to the application and popularization of flexible scheduling, especially in uncertain manufacturing environments. In this paper, we consider flexible job shop scheduling with uncertain processing time represented by fuzzy numbers, which is named fuzzy flexible job shop scheduling. We propose an effective hybrid cooperative co-evolution algorithm (hCEA) for the minimization of fuzzy makespan. The hCEA combines particle swarm optimization with the genetic algorithm to improve the convergence ability. A parameter self-adaptive strategy is applied to the problems with different scale effectively as well. Five benchmarks and three large-scale problems with fuzzy processing time are adopted to test the hCEA. Computational results show that the hCEA performs better than the existing methods from the literature.

Multiple unmanned aerial vehicle (Multi-UAV) cooperative area search is an important and effective means of intelligence acquisition and disaster rescue. Search path planning is a critical factor to improve multi-UAV search performance. Aiming at the search inefficiency resulting from insufficient cooperation between UAVs in existing researches, we present a novel distributed real-time search path planning method based on distributed model predictive control (DMPC) framework. Firstly, we formulate the overall search objective function in finite time domain, considering not only repeated searches, but also maintenance of connectivity and collision avoidance between UAVs. Secondly, we decompose the overall search objective function to establish a distributed constrained optimization problem (DCOP) model, so that all UAVs optimize the overall search objective by interacting with neighbors. Thirdly, aiming at the problem of falling into the local optima in existing algorithms, distributed stochastic algorithm based on enhanced genetic algorithm (DSA-EGA) is proposed to solve the established DCOP model. We design a point crossover operator and introduce anytime local search (ALS) framework that stores the global optimal solution explored. Finally, the simulation results of different benchmark problems demonstrate that the proposed DSA-EGA outperforms other state-of-the-art algorithms in terms of the quality of solution. The simulation results of cooperative area search problems illustrate that the established DCOP model improves the search efficiency by 7.7 $\%$ , and DSA-EGA improves the search efficiency by 4.3 $\%$ at least. In addition, we also verify that our method has high scalability.

In this paper, we aim to optimize the power allocation strategy in priority constraint WPT systems with a large number of wireless nodes for WSNs in cognitive IIOTs. The constructed WPT system consists of a rechargeable WSN and multiple mobile chargers with adequate power, which can charge the wireless nodes simultaneously. As each solution of the power allocation strategy can be represented as the directed acyclic graph (DAG), and the critical path (CP) existing in DAG is the core factor to the final maximum cost. Thus, we propose a decomposition strategy which can identify the interacting variables based on critical path by exploiting the perturbation technique. Then, the decomposed sub-components are cooperatively evolved by adopting cooperative evolutionary algorithm (CEA). The proposed CP-based grouping strategy coordinate with CEA is named as CPCEA. Three state-of-the-arts are compared with the proposed CPCEA algorithm, and the experimental results demonstrate the validity of CPCEA.

The sudden outbreak of COVID-19 brings many unpredictable situations to human travel, such as temporarily closed highways, parking lots, etc. The scenarios mentioned above will lead to a large backlog of vehicles, and the requirements of Internet of vehicle (IoV) applications increase sharply in a period of short time correspondingly. Mobile edge computing (MEC) is a key enabling technology that can guarantee the diverse requirements of IoV applications through the optimization of resource scheduling. However, the sharp increasing in requirements of IoV applications caused by the congestion of highways or parking lots still bring great challenges to the deployment of traditional MEC. Therefore, in this paper, we construct an unmanned aerial vehicle (UAV) enabled MEC system, in which the data generated from IoV applications is processed by offloading to UAVs with MEC servers to ensure the efficiency of data processing and the response time of IoV applications. In order to approximate real-world UAV enabled MEC system, we consider the stochastic offloading and downloading processing time. Moreover, the priority constraints of sensors from the same vehicle are taken into consideration since they have different importance degrees. Then, we propose an Markov network-based cooperative evolutionary algorithm (MNCEA) to search out the optimal UAV scheduling solution to guarantee the shortest response time, in which the solution space is divided into multiple sub-solution spaces with the help of MN structure and parameters. Finally, we construct multiple simulation experiments with different probability distributions to simulate uncertainty factors. The simulation results verify the validity of MNCEA compared with the state-of-the-art methods, which is reflected by the shortest response time of requirements of IoV applications

Centrality is an effective method to identify important nodes in complex networks, but it is still a challenge to find influential nodes by making full use of multiple relationships and global network topological features in complex networks. To address these problems, this article proposes an importance identification method for multilayer heterogeneous network node by incorporating multirelational information (MLC). This method studies the relational characteristics of heterogeneous nodes in detail and divides the heterogeneous nodes into different layers according to the node types, which can be further divided into core and auxiliary layers. The importance of the auxiliary layer is quantified by designing the interlayer influence and determining the interlayer influence weights of different connectivity influences; the centrality score of heterogeneous nodes under multiconnectivity relationships is fused using the transmission characteristics of internode relationships in the auxiliary layer, which in turn measures the importance of nodes in the core layer. To evaluate the proposed algorithm, we conduct experiments on five real multilayer heterogeneous networks of different sizes. The results show that MLC can make full use of different types of internode association relationship information, effectively fuse network structure information such as the neighbor weights of core and auxiliary layer nodes, and outperform the existing techniques in identifying important nodes.