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Robotics is a vast and growing area in academia and industry, and it is used to solve human problems through artificial intelligent machines. To solve crucial issues or problems such as the survivability of humans in mining, living in Antarctica, Space, mountainous regions, and other challenging terrains, we need an intelligent machine. Working with multiple robots is more beneficial than with a single robot. This article proposed a method to search and track the target in an unknown environment through a multi-agent system using a nature-inspired algorithm (NIA). Here, the location of the target is either known or unknown. To reach the target, mobile agents use two types of movement, either directional-based movement or nature-inspired algorithm-based movement. Directional-based movements help the mobile agents decide the point of interest to move. The nature-inspired algorithm (NIA) based movement helps the agents explore the area in the provided direction. This work uses particle swarm optimization (PSO), bacteria foraging optimization (BFO), and bat algorithm (BA) NIA for the exploration of the area. The proposed method has been tested in simple and complex environments. From the experimental analysis, the proposed method successfully tracks or searches the target in an unknown environment. The experiments show that the agent movement behavior is affected by some factors such as the complexity of the environment, number of agents, sensor range, etc. The results indicate that the bat algorithm outperforms the other two algorithms in terms of fast time completion. [ABSTRACT FROM AUTHOR]

In response to the energy transition to renewable resources, green hydrogen production via electrolysis is gaining momentum. Modular electrolysis plants provide a flexible and scalable solution to meet rising hydrogen demand and adapt to renewable energy fluctuations. However, optimizing their operation poses challenges, especially when dealing with heterogeneous electrolyzer modules. In this work, a combination of decentralized Multi-Agent Systems and the Module Type Package concept is presented that enhances the cost-optimized operation of modular electrolysis plants. This approach synergizes the individual strengths of Multi-Agent Systems in handling complex operational dynamics with the efficiency of the Module Type Package for integration and control capabilities. By integrating these technologies, the approach addresses the heterogeneity of electrolyzer modules and increases the adaptability, scalability, and operational flexibility of electrolysis plants. The approach was validated through a case study, demonstrating its effectiveness in achieving cost-optimized load scheduling, dynamic response to demand–supply fluctuations, and resilience against electrolyzer module malfunctions. In summary, the presented approach offers a comprehensive solution for the effective coordination and optimization of modular electrolysis plants. [ABSTRACT FROM AUTHOR]

Protection relays that incorporate advanced microprocessors are vital to electrical grids, providing fast and reliable responses to faulty conditions using efficient communication protocols. Instant detection and response to various faults are essential to minimise the risk of damage. Numerical relays can identify faulty conditions and trigger circuit breakers to open, thus preventing further damage to the system. Due to the lack of autonomous decision-making capabilities, existing numerical relays require manual reconfiguration in situations such as a change in network configuration and protection settings. These relays also do not have the ability to coordinate fault clearance when multiple sources supply power to the grid. A comprehensive overview of the research aimed at developing a multiagent system (MAS)-based adaptive protection relay will be provided by dividing it into separate articles such as firmware and hardware. This first part delves into the firmware aspects of this innovative relay, highlighting its adaptive capabilities and key considerations in its development. The second part will provide detailed design descriptions for the hardware features of the relay. An STM32MPU multicore advanced microprocessor is utilised to design and develop the adaptive numerical relay firmware. It incorporates several protection relay ANSI codes, communication protocols, and MAS-based adaptive protection schemes as part of the firmware.

This paper provides simulation studies and experiment validation of cooperative coverage control of a multi-UAV system to quickly and initially assess the environment immediately after an earthquake. Quick response of disaster management team in the early hours after the earthquake can play a significant role in reducing the degree of damages and casualties. We consider an earthquake disaster scenario wherein four fixed-wing UAVs initially fly over the affected area and perform a quick general scan to identify critical areas wherein people have suffered severe injuries or buildings have been destroyed. The multi-rotors then fly over the critical areas identified by fixed-wing UAVs, as close as possible, to extract more detailed information and exactly localize the victims and survivors or also damaged infrastructure as stationary targets in a cooperative search and coverage control problem. For the first phase of the mission, we simulate a cooperative multi-agent system comprising four fixed-wing UAVs. For the second phase of the mission, we experimentally implement cooperative search and coverage control of a multi-UAV system including three DJI Tello quadcopters to gather more detailed information about the critical section identified by fixed-wing UAVs in the first phase of the mission and find the exact location of the victims or survivors as well. In cooperative coverage control, agents communicate with each other and exchange information. Therefore, at every moment of the execution of the mission, all agents have a unique cognitive map of the environment. The comparison of cooperative and non-cooperative methods shows that the uncertainty of the environment reaches the minimum acceptable value much earlier and the maximum coverage of the area will be achieved much faster in the collaborative distributed control method. [ABSTRACT FROM AUTHOR]

This paper presents a new ontology-based multicriteria spatial group decision support system (GDSS) dedicated to site selection problems. Site selection is one of the most complex problems in the construction of a new building. It presents a crucial problem in terms of selecting the appropriate site among a group of decision makers with multiple alternatives (sites); in addition, the site must satisfy several criteria. To deal with this, the present paper introduces an ontology based multicriteria analysis method to solve semantic heterogeneity in vocabulary used by participants in spatial group decision support systems. The advantages of using ontology in GDSS are many: i) it enables the integration of heterogeneous sources of data available on the web and ii) it enables to facilitate meaning and sharing of data used in GDSS by participants. In order to facilitate cooperation and collaboration between participants in GDSS, our work aims to apply ontology at the model's structuration phase. The proposed system has been successfully implemented and exploited for a personalized environment. [ABSTRACT FROM AUTHOR]

Local energy markets (LEMs) provide opportunity to handle the challenges arising from the lower grid level while using the traditional top-down approach to manage distributed generated renewable energy resources. Blockchain-based local energy markets (LEMs) have been introduced in recent years as a way to enable local consumers/prosumers to trade their energy locally in a distributed and highly secured manner in an LEM. However, there are still some challenges regarding the main factors that can drive local consumers/prosumers to participate in a blockchain-based LEM, optimal community size, and prosumer to consumer ratio for an efficient LEM. Also, there is still no information on how the quantifying factors for participation on a blockchain-based LEM can affect the performance of an LEM. This paper presents a survey and simulation based analysis of quantifying factors for participation in a blockchain-based LEM. The survey was distributed among local consumers/prosumers and a total of 261 responses were received from the responders. The results from the responders were analyzed using a Python code based statistical analysis model. The simulation based analysis was conducted using a community based LEM model and evaluated using data received from a combination of German household profiles and standard load profiles. The survey results showed that the major drive for local consumers/prosumers to participate on blockchain-based LEM is their willingness to support renewable energy integration, transparency, and trust offered by a blockchain network. On the other hand, the simulation based analysis showed that small and medium communities with prosumers to consumer ratios between 0.3 to 0.5 create more economic and technical benefits for local consumers/prosumers compared to large communities. The community based simulation results were modelled together with the survey results to determine how the individual quantifying factors for participating in a blockchain-based LEM can affect the performance of an LEM.

Escalating energy demands and climate change challenges necessitate the adaptation of renewable-based microgrid systems in the energy sector. The proposed work employs a robust Multi Agent System (MAS) technique to achieve efficient and automated control of the hybrid microgrid operation. The hybrid microgrid system incorporates Renewable Energy Sources (RES), a diesel generator, and a battery storage system. The operation of the hybrid microgrid consists of three distinct modes: islanded, transition to grid, and grid-oriented mode. The system’s performance is optimized by considering factors like climatic patterns, energy costs, connected source characteristics, and load demand. Different climatic scenarios are assessed for each mode of operation, where the best, extreme sunny, extreme cloudy, and worst climate conditions are considered for islanded mode; sunny and cloudy scenarios are considered for transition to grid mode as well as grid-feed and grid-tied modes are considered for grid-oriented operation of the microgrid. The simulation studies are performed using the MATLAB/Simulink R2021a environment. Furthermore, Particle Swarm Optimization (PSO) is implemented to optimize power allocation within the microgrid and enhance its cost-effectiveness. The optimization results demonstrate efficient utilization of available energy sources along with effective energy management facilitated by the MAS control system. The results emphasize the importance of adopting a MAS approach for achieving smart energy management through comprehensive analysis and integrating decentralized energy management techniques for optimal accommodation of distributed energy resources in hybrid microgrids.

The internet of agents (IoA) is an emergent model, which mainly intends to resolve the limitations of the internet of things (IoT) devices with respect to reasoning and social competencies for improving proactivity, intelligence, and interoperability. This article presents a novel grasshopper optimization with deep learning enabled multi‐agent system for age and gender classification (GOADL‐MASAGC) model for IoA. The proposed GOADL‐MASAGC technique intends to categorize age as well as gender. The proposed GOADL‐MASAGC technique applies a multi‐agent system, which incorporates distinct processes for age and gender classification like pre‐processing, feature extraction, and classification. Besides, the GOADL‐MASAGC technique enables to performance concurrent process of classification and regression to identify age and gender respectively. In addition, the GOA with Capsule Network (CapsNet) model was executed for deriving a suitable group of feature vectors and the GOA is employed as a hyperparameter optimizer. Finally, wavelet kernel extreme learning machine (WKELM) was employed as a classifier for gender classification and deep belief network (DBN) is used as a regression approach for age recognition. For demonstrating the improved performance of the GOADL‐MASAGC model, a series of simulations were executed and the outcomes are examined in various aspects. The extensive comparative analysis reported the enhanced outcomes of the GOADL‐MASAGC approach over the existing methods. [ABSTRACT FROM AUTHOR]

Effective risk management reaction improves the absorption of critical impacts on supply chains. Supply chain risk (SCR) sources, like control, process, demand, and supply, need to be identified, assessed, and mitigated to make rational decisions immediately. Late detection of a disruptive event can cause delays in handling risk. Since SCRs consist of complex, uncertain, and incomplete information, most of the provided risk assessment mechanisms cannot handle it in real-time. Hence, in this paper, we introduce NeutroMAS4SCRM, a framework incorporating real-time Multi-Agent Systems (MAS) with Neutrosophic Data Analytic Hierarchy Processes to best deal with the complexity, uncertainty, and vagueness of SCR management-related issues and which can hence help decision-makers adopt less risky decisions. In addition, the MAS technology contribution to SCR management is outlined through a comparative study among the most recent studies. In contrast, the proposed MAS for the supply chain is implemented under the JADE agent platform, where the FIPA-ACL-based message content is specified using a dedicated ontology. A simulation-based decision support system is used to assess the cost risk and its harmful effects and determine how well the proposed framework can help companies manage risks efficiently. The simulation has proven to reduce risk costs by about 85%. [ABSTRACT FROM AUTHOR]

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The power generation industry needs to adopt renewable energy so as to reduce the utilization of fossil energy and pollution emission. In renewable energy power generation, microgrid operation optimization needs to consider multiple objectives such as economy and environmental protection, which is a multi-objective optimization problem. Aiming at the multi-objective optimization problem, based on the Pareto optimal concept, a hybrid crossover multi-agent multi-objective evolutionary algorithm is proposed and applied to the multi-objective optimization problem of microgrid systems, in which the economical cost and environmental protection are considered. The simulation results under three operating conditions show that compared with the classical NSGA-â ¡ algorithm, the proposed algorithm can obtain higher quality Pareto optimal solution in a shorter time. The efficiency of the proposed algorithm in this problem is higher than that of the classical NSGA-â ¡ algorithm. It can provide a higher quality solution for the optimal operation of a microgrid. [ABSTRACT FROM AUTHOR]

This paper is concerned with the control of Lipschitz continuous multi-agent systems in the presence of actuator faults. The faults in a multi-agent system can potentially affect the mission and lead to instability. A linear matrix inequality based method is proposed to account for the faults so that a stable consensus can be maintained even when the agents are subject to hybrid faults and nonlinearities. Two illustrative examples are provided to show the design and feasibility of the proposed method.

Vehicular Ad-Hoc Networks (VANET) models are derived from Mobile Ad-Hoc Networks that possess an impressive role in the intelligent transportation system today and also in the future. So scientists are always looking for the best theories in hope of grasping the fastest and safest connections between vehicles, to provide traffic and accident news in real time. The high number of nodes is one of the significant features of this network that results in an increase of the number of transmitted packets in the network. Distributed system and high mobility of the nodes are the other most important attributes of Vehicular Ad-Hoc networks. In this paper, we aim to review the studies and improvements of vehicular Ad-Hoc network in the fields of safety and traffic management. In addition, we introduce the most common challenges of this network and gather latest methods for resolving those problems. Multi agent systems will be one of the newest and suitable tools in this system. The main purpose is arranging safe connection between agents. Fixing agreements and removing the disputes are not detachable from functional system. It is like group associations and societies which require human collaborations and cooperation. Development of science resulted in utilization of these systems in various aspects like VANET. Nowadays scientists and researchers are interested to make use of these methods for improving the role of vehicle networks in the field of traffic management, safety and entertainment, we will review them in the following paragraphs. [ABSTRACT FROM AUTHOR]

Covid-19, which first appeared in Wuhan, spread worldwide. In Europe, Italy and Spain were the first to be hit, before the entire continent was. This health crisis has affected the whole world and at all levels (hospital capacity, number of health workers, shortage of medicines, ...). With 5,318,216 deaths worldwide as of December 16, 2021, the disease has shown that the health-care system is not sufficiently prepared to cope with the coronavirus. COVID 19, like any other disease, requires a follow-up of its evolution to reduce its harmful effects. In order to ensure the comfort to the patients by offering them a care path adapted to their situation, the exploitation of artificial intelligence within the hospital is necessary and this by approaching the human cognition in the analysis of complex medical data. The objective of this study is to generate the patient's care pathway from his initial state at the first consultation with the physician. To do this, we will have to optimize the hospital supply chain by modeling the patient's care pathway. First, we will proceed by identifying all the entities contributing to the hospital logistics chain. Then, the supply chain will be optimized thanks to the use of lean to deliver to the patient what he needs when he needs it to accomplish its care. Finally, the Multi Agents method will be used to model all the parts concerning the course of the treatment (radiology, surgery, pharmacy, accounting, ...) and to show the interactions that can take place to make them as effective as possible. [ABSTRACT FROM AUTHOR]

In recent years, local energy markets (LEMs) have been introduced to empower end-customers within energy communities at the distribution level of the power system, in order to be able to trade their energy locally in a competitive and fair environment. However, there is still some challenge with regard to the most efficient approach in organising the LEMs for the electricity exchange between consumers and prosumers while ensuring that they are responsible for their electricity-related choices, and concerning which LEM model is suitable for which prosumer or consumer type. This paper presents a hierarchical model for the organisation of agent-based local energy markets. According to the proposed model, prosumers and consumers are enabled to transact electricity within the local energy community and with the grid in a coordinated manner to ensure technical and economic benefits for the LEM's agents. The model is implemented in a software tool called Grid Singularity Exchange (GSyE), and it is verified in a real German energy community case study. The simulation results demonstrate that trading electricity within the LEM offers economic and technical benefits compared to transacting with the up-stream grid. This can further lead to the decarbonization of the power system sector. Furthermore, we propose two models for LEMs consisting of multi-layer and single-layer hierarchical agent-based structures. According to our study, the multi-layer hierarchical model is more profitable for household prosumers as compared to trading within the single-layer hierarchical LEM. However, the single-layer LEM is more be beneficial for industrial prosumers. [ABSTRACT FROM AUTHOR]

Static risk analysis techniques (SRATs) use event graphs and risk analysis assessment models. Those techniques are not time-based techniques and hence are inadequate to model dynamic stochastic systems. This paper proposes a novel dynamic approach to model such stochastic systems using Dynamic Fault Trees (DFT). The proposed model is called Generic Dynamic Agent-Based Model (GDABM) for risk analysis. GDABM is built on top of the well-known Agent-Based Modeling and Simulation (ABMS) technique. GDABM can model the dynamic system agents in both nominal (failure-free) and degraded (failure) modes. GDABM shows the propagation of failure between system elements and provides complete information about the system's configurations. In this paper, a complete detailed case study is provided to show the GDABM capabilities to model and study the risk analysis for such dynamic systems. In the case study, the GDABM models the risk analysis for a chemical reactor/operator and performs a complete risk analysis for the entire system. The GDABM managed to simulate the dynamic behavior of the system's components successfully using Repast Simphony 2.0. Detailed agent behavioral modes and failure modes are provided with various scenarios, including different time stamps. The proposed GDABM is compared to a reference model. The reference model is referred to as the ABM model. GDABM has given very promising results. A comparison study was performed on three performance measures. The performance measures used are (1) Accuracy, (2) response time, and (3) execution time. GDABM has outperformed the reference model by 15% in terms of accuracy and by 27% in terms of response time. GDABM incurs a slightly higher execution time (13%) when compared to the ABM reference model. It can be concluded that GDABM can deliver accepted performance in terms of accuracy and response time without incurring much processing overhead. [ABSTRACT FROM AUTHOR]

This paper describes a solution to the problem of consensus/synchronisation for a general class of networked nonlinear multi-agent systems (MAS) using a distributed control strategy with an event-triggered communications (ETC) mechanism. We consider the case where the dynamics of each agent contain linear and Lipschitz nonlinear terms and the underlying communication graph is directed. The strategy proposed has two important properties: (i) it achieves practical consensus, i.e. the synchronisation error that measures the disagreement among the agents' states converges to a ball centred at the origin, with a radius that can be made arbitrarily small and (ii) the minimum of the inter-event times for each agent is lower bounded by a strictly positive number, hence Zeno behaviour is excluded. Furthermore, it affords system designers adequate tools to trade off the frequency of communications among agents against the level of performance achieved in MAS consensus/synchronisation. Numerical simulation results are also given. [ABSTRACT FROM AUTHOR]

An intelligent system intended to provide assistance to an elderly user in their home is presented. This multi-agent system is designed to monitor the interaction between the user and other agents, and regulate itself based on the estimated level of mental engagement of the user in order to contribute to help them maintain adequate levels of awareness and interaction with their home environment. The agents are devices programmed to preserve the security, comfort and health of the user, who is also conceptualized as one of the agents in the system. The user engagement level is estimated through fuzzy inference, that is, by means of a set of fuzzy membership functions and rules applied over the interaction of the user with different categories of agents. The system incorporates several dozen agents and is tested using two different datasets of real interactions between users and devices in their home environments. A uniform parameter configuration yielded very positive results for diverse scenarios and subsequently the parameters of the system were automatically optimized to show that improved performance, in the form of a more efficient system regulation and increased certainty in adjustment estimation, is obtained. [ABSTRACT FROM AUTHOR]

The rising incursions with embed and linked sensors cause the construction of the Internet of Things (IoT). It's presenting a wide access to the reality. We support that on head of this data, a WoT is required. A framework that permits the achievement of programs related to sensors in the IoT. In this paper we present a conceptual model that describes Web of Things Search engine (WoTSE). Our WoTSE is based on artificial multi agent system with a modular architecture, addressing the challenge of scalable search for fast altering content while benefiting on the existing internet structure. Furthermore, we propose a conceptual search framework for the Web of Things, "WoTmAS2E", with experimental evaluation using a prototype. [ABSTRACT FROM AUTHOR]

Robotization is increasingly used in the agriculture since the last few decades. It is progressively replacing the human workforce that is deserting the agricultural sector, partly because of the harshness of its activities and health risks they may present. Moreover, robotization aims to improve efficiency and competitiveness of the agricultural sector. However, it leads to several research and development challenges regarding robots supervision, control and optimization. This paper presents a simulation and optimization approach for the optimization of robotized treatment tasks using type-c ultraviolet radiation in horticulture. The optimization of tasks scheduling problem is formalized and a heuristic and a genetic algorithms are proposed to solve it. These algorithms are evaluated compared to an exact method using a multi-agent-based simulation approach. The simulator takes into account the evolution of the disease during time and simulates the execution of treatment tasks by the robot. [ABSTRACT FROM AUTHOR]

This paper proposes the development of a formation control algorithm of multiple acoustic underwater vehicles by employing the behaviour of autonomous mobile agents under a proposed pursuit. A robust pursuit is developed using the distributed consensus coordinated algorithm ensuring the transfer of information among the AUVs. The development of robust pursuit based on characteristics of multi-agent system is for solving the incomplete information capabilities in each agent such as asynchronous computation, decentralized data and no system global control. In unreliable and narrow banded underwater acoustic medium, the formation of AUVs based distributed coordinated consensus tracking can be accomplished under the constant or varying virtual leader’s velocity. Further, the study to achieve tracking based on virtual leader AUV’s velocity is extended to fixed and switching network topologies. Again for mild connectivity, an adjacency matrix is defined in such a way that an adaptive connectivity is ensured between the AUVs. The constant virtual leader vehicle velocity method based on consensus tracking is more robust to reduce inaccuracy because no accurate position and velocity measurements are required. Results were obtained using MATLAB and acquired outcomes are analysed for efficient formation control in presence of the underwater communication constraints.

UAVs have rapidly become prevalent in applications related to surveillance, military operations, and disaster relief. Their low cost, operational flexibility, and unmanned capabilities make them ideal for accomplishing tasks in areas deemed dangerous for humans to enter. They can also accomplish previous high-cost and labor-intensive tasks, such as land surveying, in a faster and cheaper manner. Researchers studying UAV applications have realized that a swarm of UAVs working collaboratively on tasks can achieve better results. The dynamic work environment of UAVs makes controlling the vehicles a challenge. This is magnified by using multiple agents in a swarm. Resiliency is a broad concept that effectively defines how well a system handles disruptions in its normal functioning. The task of building resilient swarms has been attempted by researchers for the past decade. However, research on current trends shows gaps in swarm designs that make evaluating the resiliency of such swarms less than ideal. The authors believe that a complete well-defined system built from the ground up is the solution. This survey evaluates existing literature on resilient multi-UAV systems and lays down the groundwork for how best to develop a truly resilient system.

The open-pit mining operational planning problem (OPMOP) is approached by applying Multi-Agent Systems (MAS) to optimize task distribution and find the most efficient paths simultaneously. Several agents are being investigated, including several trucks with different capabilities and operating costs, as well as shovels with differing production rates and compatibility. As open pit mines have a wide range of materials, shovels are assigned to them based on their characteristics. In addition, autonomous agents must be evaluated and optimized to use minimal resources while adhering to constraints. Simulations were done to assess how the system compares to human experience by using the IoT and online data to create a successful final schedule and plan. [ABSTRACT FROM AUTHOR]

The problem treated in this work is the management of the home health care service. Especially, we studied the caregivers' tours problem and the management of conflict sanitary visits. Home health care service is known as a care mode allowing patients suffering from evolving and complex diseases to benefit at home from medical and paramedical coordinated care that can be only provided in care institution. To solve this problem we proposed a multi-agents approach using three heuristics which optimize both the daily caregivers' tours to minimize the travel costs and maximize the planned services in order to address potential conflicts. This approach was tested on as set of benchmarks from the literature and shows the efficiency of the proposed algorithms. After studying the various numerical calculated results, we concluded that the policy based on «the number of remaining patient to visit» gives better results in terms of resolving detected conflicts. [ABSTRACT FROM AUTHOR]