Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

Shall we always use hydraulic models? A graph neural network metamodel for water system calibration and uncertainty assessment

Representing reality in a numerical model is complex. Conventionally, hydraulic models of water distribution networks are a tool for replicating water supply system behaviour through simulation by means of approximation of physical equations. A calibration process is mandatory to achieve plausible simulation results. However, calibration is affected by a set of intrinsic uncertainty sources, mainly related to the lack of system knowledge. This paper proposes a breakthrough approach for calibrating hydraulic models through a graph machine learning approach. The main idea is to create a graph neural network metamodel to estimate the network behaviour based on a limited number of monitoring sensors. Once the flows and pressures of the entire network have been estimated, a calibration is carried out to obtain the set of hydraulic parameters that best approximates the metamodel. Through this process, it is possible to estimate the uncertainty that is transferred from the few available measurements to the final hydraulic model. The paper sparks a discussion to assess under what circumstances a graph-based metamodel might be a solution for water network analysis.

UAV and IoT-Based Systems for the Monitoring of Industrial Facilities Using Digital Twins: Methodology, Reliability Models, and Application

This paper suggests a methodology (conception and principles) for building two-mode monitoring systems (SMs) for industrial facilities and their adjacent territories based on the application of unmanned aerial vehicle (UAV), Internet of Things (IoT), and digital twin (DT) technologies, and a set of SM reliability models considering the parameters of the channels and components. The concept of building a reliable and resilient SM is proposed. For this purpose, the von Neumann paradigm for the synthesis of reliable systems from unreliable components is developed. For complex SMs of industrial facilities, the concept covers the application of various types of redundancy (structural, version, time, and space) for basic components-sensors, means of communication, processing, and presentation-in the form of DTs for decision support systems. The research results include: the methodology for the building and general structures of UAV-, IoT-, and DT-based SMs in industrial facilities as multi-level systems; reliability models for SMs considering the applied technologies and operation modes (normal and emergency); and industrial cases of SMs for manufacture and nuclear power plants. The results obtained are the basis for further development of the theory and for practical applications of SMs in industrial facilities within the framework of the implementation and improvement of Industry 4.0 principles.

A Multi-Colony Social Learning Approach for the Self-Organization of a Swarm of UAVs

This research offers an improved method for the self-organization of a swarm of UAVs based on a social learning approach. To start, we use three different colonies and three best members i.e., unmanned aerial vehicles (UAVs) randomly placed in the colonies. This study uses max-min ant colony optimization (MMACO) in conjunction with social learning mechanism to plan the optimized path for an individual colony. Hereinafter, the multi-agent system (MAS) chooses the most optimal UAV as the leader of each colony and the remaining UAVs as agents, which helps to organize the randomly positioned UAVs into three different formations. Afterward, the algorithm synchronizes and connects the three colonies into a swarm and controls it using dynamic leader selection. The major contribution of this study is to hybridize two different approaches to produce a more optimized, efficient, and effective strategy. The results verify that the proposed algorithm completes the given objectives. This study also compares the designed method with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to prove that our method offers better convergence and reaches the target using a shorter route than NSGA-II.

A Survey of Multi-Agent Cross Domain Cooperative Perception

Intelligent unmanned systems for ground, sea, aviation, and aerospace application are important research directions for the new generation of artificial intelligence in China. Intelligent unmanned systems are also important carriers of interactive mapping between physical space and cyberspace in the process of the digitization of human society. Based on the current domestic and overseas development status of unmanned systems for ground, sea, aviation, and aerospace application, this paper reviewed the theoretical problems and research trends of multi-agent cross-domain cooperative perception. The scenarios of multi-agent cooperative perception tasks in different areas were deeply investigated and analyzed, the scientific problems of cooperative perception were analyzed, and the development direction of multi-agent cooperative perception theory research for solving the challenges of the complex environment, interactive communication, and cross-domain tasks was expounded.

Control and Optimization of Multi-Agent Systems and Complex Networks for Systems Engineering

Systems engineering crosses multiple engineering disciplines for the design, control, and overall management of engineered systems [...]

Multi-Agent Systems in Fog-Cloud Computing for Critical Healthcare Task Management Model (CHTM) Used for ECG Monitoring

In the last decade, the developments in healthcare technologies have been increasing progressively in practice. Healthcare applications such as ECG monitoring, heartbeat analysis, and blood pressure control connect with external servers in a manner called cloud computing. The emerging cloud paradigm offers different models, such as fog computing and edge computing, to enhance the performances of healthcare applications with minimum end-to-end delay in the network. However, many research challenges exist in the fog-cloud enabled network for healthcare applications. Therefore, in this paper, a Critical Healthcare Task Management (CHTM) model is proposed and implemented using an ECG dataset. We design a resource scheduling model among fog nodes at the fog level. A multi-agent system is proposed to provide the complete management of the network from the edge to the cloud. The proposed model overcomes the limitations of providing interoperability, resource sharing, scheduling, and dynamic task allocation to manage critical tasks significantly. The simulation results show that our model, in comparison with the cloud, significantly reduces the network usage by 79%, the response time by 90%, the network delay by 65%, the energy consumption by 81%, and the instance cost by 80%.

Cluster-Delay Mean Square Consensus of Stochastic Multi-Agent Systems with Impulse Time Windows

This paper investigates the cluster-delay mean square consensus problem of a class of first-order nonlinear stochastic multi-agent systems with impulse time windows. Specifically, on the one hand, we have applied a discrete control mechanism (i.e., impulsive control) into the system instead of a continuous one, which has the advantages of low control cost, high convergence speed; on the other hand, we considered the existence of impulse time windows when modeling the system, that is, a single impulse appears randomly within a time window rather than an ideal fixed position. In addition, this paper also considers the influence of stochastic disturbances caused by fluctuations in the external environment. Then, based on algebraic graph theory and Lyapunov stability theory, some sufficiency conditions that the system must meet to reach the consensus state are given. Finally, we designed a simulation example to verify the feasibility of the obtained results.

Building Robust Closed-Loop Supply Networks against Malicious Attacks

With recent industrial upgrades, it is essential to transform the current forward supply networks (FSNs) into closed-loop supply networks (CLSNs), which are formed by the integration of forward and reverse logistics. The method chosen in this paper for building reverse logistics is to add additional functions to the existing forward logistics. This process can be regarded as adding reverse edges to the original directed edges in an FSN. Due to the limitation of funds and the demand for reverse flow, we suppose that a limited number of reverse edges can be built in a CLSN. To determine the transformation schemes with excellent robustness against malicious attacks, this paper proposes a multi-population evolutionary algorithm with novel operators to optimize the robustness of the CLSN, and this algorithm is abbreviated as MPEA-RSN. Then, both the generated and realistic SNs are taken as examples to validate the effectiveness of MPEA-RSN. The simulation results show that the index R, introduced to evaluate the robustness of CLSNs, can be improved by more than 95%, and this indicates that (1) the different schemes for adding reverse routes to an FSN can lead to different robustness values, and (2) the robustness of the transformed CLSN to malicious attacks can be significantly improved after optimization by MPEA-RSN. When an FSN is to be transformed into a CLSN, this paper can provide a frame of reference for building a CLSN that is robust to malicious attacks from a network structural perspective.

An Event-Driven Agent-Based Simulation Model for Industrial Processes

Process manufacturing industries are complex and dynamic systems composed of several processes, subject to many operations and unexpected events that can compromise overall system performance. Therefore, the use of technologies and methods that can transform traditional process industries into smart factories is necessary. In this paper, a smart industrial process based on intelligent software agents is presented with the aim of providing a technological solution to the specific needs of the process industry. An event-driven agent-based simulation model composed of eight reactive agents was designed to simulate and control the operations of a generic industrial process. The agents were modeled using the actor approach and the communication mechanism was based on the publish–subscribe paradigm. The overall system was tested in different scenarios, such as faults, changing operating conditions and off-spec productions. The proposed agent-based simulation model proved to be very efficient in promptly reacting to different dynamic scenarios and in suitably handling different situations. Furthermore, the usability and the practicality of the proposed software tool facilitate its deployment and customization to different production chains, and provide a practical example of the use of multi-agent systems and artificial intelligence in the context of industry 4.0.