Cooperative Algorithm Research Articles

The cooperative algorithm with auxiliary objectives for the truck and trailer routing problem

<p>In this paper, a cooperative algorithm with auxiliary objectives is proposed to resolve the truck and trailer routing problem. In this proposal, each member of the population does not represent a complete solution as in almost any evolutionary algorithm. In addition, for each member, an aptitude is not possible to compute based only on its codification, because the member has only partial information of the solution. All the members of the population have partial information of the solution. Therefore, these members need to cooperate to obtain an aptitude for the entire population. This way of computing fitness is clearly a gap in the literature, and must be investigated. Moreover, the multi-objectivization approach incorporates an important feature to the proposed algorithm in order to improve its performance, i.e., the multi-objectivization approach permits to identify the best trips using the auxiliary objectives. Enough experimental results are shown that the cooperative algorithm is competitive against other current evolutionary algorithms. There no exist statistically significant difference between the cooperative algorithm and the others.</p>

A FDA-based multi-robot cooperation algorithm for multi-target searching in unknown environments

Target search using a swarm of robots is a classic research topic that poses challenges, particularly in conducting multi-target searching in unknown environments. Key challenges include high communication cost among robots, unknown positions of obstacles, and the presence of multiple targets. To address these challenges, we propose a novel Robotic Flow Direction Algorithm (RFDA), building upon the modified Flow Direction Algorithm (FDA) to suit the characteristics of the robot’s motion. RFDA efficiently reduces the communication cost and navigates around unknown obstacles. The algorithm also accounts for scenarios involving isolated robots. The pipeline of the proposed RFDA method is outlined as follows: (1). Learning strategy: a neighborhood information based learning strategy is adopted to enhance the FDA’s position update formula. This allows swarm robots to systematically locate the target (the lowest height) in a stepwise manner. (2). Adaptive inertia weighting: An adaptive inertia weighting mechanism is employed to maintain diversity among robots during the search and avoid premature convergence. (3). Sink-filling process: The algorithm simulates the sink-filling process and moving to the aspect slope to escape from local optima. (4). Isolated robot scenario: The case of an isolated robot (a robot without neighbors) is considered. Global optimal information is only required when the robot is isolated or undergoing the sink-filling process, thereby reducing communication costs. We not only demonstrate the probabilistic completeness of RFDA but also validate its effectiveness by comparing it with six other competing algorithms in a simulated environment. Experiments cover various aspects such as target number, population size, and environment size. Our findings indicate that RFDA outperforms other methods in terms of the number of required iterations and the full success rate. The Friedman and Wilcoxon tests further demonstrate the superiority of RFDA.

An efficient algorithm for resource optimization in TWDM passive optical network using a C-RAN

The traditional base station in C-RAN is divided into three parts: a pool of centralized baseband units (BBUs), a fronthaul network that links the BBUs and remote radio units (RRUs), and RRUs. This paper proposes a novel cooperative algorithm for resource optimization in a time-wavelength division multiplexed (TWDM) passive optical network (PON) incorporating a cloud radio access network (C-RAN). First, a joint collaborative strategy is deployed to optimize cooperative caching and transmission in the wireless and optical domains. Then, the quality of experience (QoE) is improved by bandwidth configuration and caching. Simulation results show that the average throughput of the proposed QoE-aware video cooperative caching and transmission mechanism (QACCTM) algorithm is approximately 30% higher than that of other algorithms. Compared with the relative average residual clutter power (RARCP) and quality-aware wireless edge caching (QAWEC) algorithms, the proposed QACCTM algorithm reduces the access delay by approximately 27.1% and 15.9%, respectively.

A Study for Comparative Analysis of Dueling DQN and Centralized Critic Approaches in Multi-Agent Reinforcement Learning

In this study, we introduce a deep Q-network agent utilizing a dueling architecture to refine the valuation of actions through separate estimations of the state-value and action-value functions, adapted to facilitate concurrent multi-agent operations within a shared environment. Inspired by the self-organized, decentralized cooperation observed in natural swarms, this study uniquely integrates a centralized mechanism, or a centralized critic. This enhances performance and coherence in decision-making within the multi-agent system. This hybrid approach enables agents to execute informed and optimized decisions by considering the actions of their counterparts while maintaining an element of collective and flexible task-information sharing, thereby presenting a groundbreaking framework for cooperation and information sharing in swarm robot systems. To augment the communication capabilities, we employ low-power wide-area networks, or Long Range (LoRa), which are characterized by their low power consumption and long-range communication abilities, facilitating the sharing of task information and reducing the load on individual robots. The aim is to leverage LoRa as a communication platform to construct a cooperative algorithm that enables efficient task-information sharing among groups. This can provide innovative solutions and promote effective cooperation and communication within multi-agent systems, with significant implications for industrial and exploratory robots. In conclusion, by integrating a centralized system into the proposed model, this approach successfully enhances the performance of multi-agent systems in real-world applications, offering a balanced synergy between decentralized flexibility and centralized control.

VSFL: Trajectory prediction framework based on validity-aware semi-asynchronous federated learning in internet of vehicles

Accurate prediction of vehicle trajectories on the Internet of Vehicles is crucial for improving traffic safety. Federated learning has been proposed to address the issue of islands in shared data. Traditional synchronous federated learning requires waiting for each node to complete training before model aggregation can occur, and the aggregation algorithm of the server cannot adjust the aggregation weight in real-time, which leads to increased communication overhead, time cost, and decreased accuracy. To balance these issues, this paper proposes a Validity-aware Semi-asynchronous Federated Learning-based framework for predicting vehicle trajectories. The framework makes the following improvements - firstly, based on the asynchronous cooperation algorithm of generalization research, the models of vehicle nodes within the same RoadSide Unit (RSU) range are aggregated into this RSU to reduce communication overhead and time costs. To further ensure the privacy and security of all participants, we added differential privacy algorithms during the model training process. Secondly, the server uses a weighted aggregation algorithm based on effective perception to aggregate all RSU models into a global model to address potential accuracy issues. This article uses a Social-LSTM like model to validate the effectiveness of this framework in the Next Generation Simulation (NGSIM) Vehicle Trajectories and Support Data dataset. Compared with traditional federated averaging algorithms and trust aware federated learning algorithms, this framework reduces distance errors by 27.80 % and 19.23 %, respectively, and improves accuracy by 22.56 % and 13.59 %.

A Q-learning based iterated local search algorithm for human-UAV cooperation in restoring transmission network

The power transmission network is easily to be destroyed when natural or man-made disasters occur. Restoration of power supply under disaster environments faces difficulties since a large-scale network typically contains many uninspected faulty nodes. Utilizing unmanned aerial vehicles (UAVs) to inspect these unknown faulty nodes can significantly improve the efficiency for subsequent restoration work performed by human-teams. Nevertheless, efficient cooperation of UAV and human-team is a complicated work due to the complexity of network structure and correlation between UAV scheduling and human-team scheduling. In this paper, a mathematical model is established to describe the considered problem aiming at maximizing the restored power supply in a limited response time. Then a Q-learning based iterated local search (Q ILS) algorithm is proposed to formulate the collaborative scheduling problem. Firstly, an initialization method is designed to assign UAVs for inspecting unknown faulty nodes and human-teams for repairing faulty nodes, which ensures each unknown faulty node is inspected before maintenance. Secondly, searching operators including perturbation and local search procedures are designed to ensure exploration and exploitation capability. Thirdly, Q-learning method is utilized as a learning engine to guide the direction of solution evolution. Moreover, the parameters of Q ILS are calibrated by multi-factor analysis of variance method to determine proper values. The computational simulations and comparison experiments validate the superiority of proposed algorithm.

Municipal Defensive Walls in Western Poland: The Example of Żagań

ABSTRACT Many relics of municipal fortifications are preserved in multiple cities in the Silesian — Lusatian region. Research works are mainly focused on area such a history, architecture or technical state, without correlation between these areas. The correct approach is to identify walls, or sections of them, as part of parallel, interdisciplinary research that covers the history and chronology of transformations, expansion phases, and their state of preservation which was also the primary and main goal of the research in case of Żagań municipal defensive walls. Thanks to architectural and archaeological analysis, it was possible to determine the chronology of the transformations and layers of the walls. Combining the results of the chronological analyses (C-14, dendrochronology) of wood samples from putlog holes with the results of architectural research allows hypotheses regarding the chronology of constructing objects to be confirmed or excluded. During the study it was found, that it is recommended to correlate the results of archaeological research conducted within the area of excavations next to the foundations of city walls with the results of the structural analyses of soil and water conditions. It is then possible to conduct a comparative analysis of the identified soil and anthropogenic layers. The correlation of architectural research in the field of the chronology of the construction of city walls with the results of the analyses of the cross-sectional deformation of their structure (based on 3D laser scanning) enables answers about the causes of the walls’ inclination to be obtained. The proposed algorithm for interdisciplinary cooperation in research of historical municipal walls is presented.

Computer multimedia aided design and hand-drawn effect analysis based on grid resource sharing cooperative algorithm

Hand-drawn is one of the few visual descriptors that can directly represent visual content, and has significant research in the area of computer vision. Aiming at the problem of sparse features in the realm of hand-drawn image retrieval, hand-drawn images, and the easy deformation of hand-drawn images, this paper proposes a feature extraction method of grid resource sharing collaborative algorithm, which can be obtained utilizing precisely extracted semantic characteristics from hand-drawn images through computer multimedia-aided design Efficient and accurate retrieval results. First, the fundamental framework for obtaining semantic features is algorithm; then the attention model mechanism is the grid resource sharing collaborative introduced in the process of supervised training, and the attention structure block is introduced after the convolutional neural network’s bottom layer. To locate effective semantic features, In order to accomplish high-precision retrieval, the attention structure block combines channel attention structure and spatial attention structure to build the attention structure block. The last feature descriptor is then created by combining various semantic feature levels. The proposed strategy is practical and efficient, as demonstrated by the experimental findings on the comparison database Flickr15k. In addition, in the task of hand-drawn image classification, the proposed attention mechanism greatly improves the classification accuracy.

Bi-objective carbon-efficient distributed flow-shop scheduling with multistep electricity pricing

A bi-objective distributed flow-shop scheduling problem with multistep electricity pricing and carbon emissions is studied. One objective is to minimise the completion time of production, and the other is to minimise the total cost of multistep electricity pricing and carbon emissions. A mixed integer programming model and a two-stage knowledge based cooperative algorithm with a local reinforcement strategy are proposed for the problem. The extensive numerical experiments show that the two-stage algorithm was effective statistical significantly in generating non-dominated solution sets and Pareto frontiers. Simulations on Electricity Prices are applied to examine different multistep electricity pricing schemes, and management implications were drawn for both government and companies.

Regional Perspective of Using Cyber Insurance as a Tool for Protection of Agriculture 4.0

The digitalization of the agricultural industry is manifested through the active use of innovative technologies in all its areas. Agribusiness owners have to constantly improve their security to meet new challenges. In this context, the existing cyber risks of the agrarian industry were assessed and their classification by possible consequences, such as data theft or alteration, cyber terrorism, cyber warfare, software hacking or modification, the blocking of markets and transactions on them, was proposed. Cyber insurance is an effective tool for minimizing the likelihood of cyber incidents and for comprehensive post-incident support, with the involvement of cybersecurity specialists. An algorithm for cooperation between an agricultural company and an insurance company when concluding a cyber risk insurance contract is proposed, which takes into account the needs and wishes of insurers at each stage of the interaction. To assess the need to use cyber insurance in agriculture 4.0, a methodology has been developed to evaluate the regional characteristics of cybersecurity and the digitalization of agribusiness. The results of the study show a heterogeneous need for this tool in different regions of the world.

Innovative multi-dimensional learning algorithm and experiment design for human-robot cooperation

This paper proposed a learning algorithm for human-robot skill transfer based on dynamic movement primitive. Through the establishment of Transmission Control Protocol communication, the position trajectory and force characteristic of the human demonstrator while demonstrating the robot were recorded as the training data of the dynamic movement primitive model. The demonstrated skill/motion reproduction and generalization were successfully achieved through adjusting the parameters of the dynamic movement primitive model. The impedance control system investigated in this paper improved the compliance of the robot trajectory reproduction and skill generalization, and improved the robustness of robot control system in different environments. In addition, for the problem of inverse solution of redundant robots, this paper improved the dimension of constraint equations in the inverse solution of robots by training multiple dynamic movement primitive models, which guaranteed the constraints of inverse solutions of redundant robots. The proposed algorithm had been validated on a simulated robot platform using MATLAB and a ROKAE xMate robot platform, respectively.

Differential Game-Based Control for Nonlinear Human-Robot Interaction System With Unknown Desired Trajectory.

Differential game is an effective technique to describe the negotiation between the humans and robots, which is widely used to realize the trajectory tracking tasks in the human-robot interaction (HRI). However, most existing works consider the control-affine HRI systems and assume the desired trajectory is available to both the human and the robot, which limit the scope of applications. To overcome these difficulties, this work focuses on the nonaffine HRI system and supposes that the desired trajectory is not available to the robot. A novel differential game framework encoding the desired trajectory estimator is proposed, where the desired trajectory is estimated via the Gaussian process regression (GPR) technique. To address the challenge arising from the nonlinearity of the HRI system, we equivalently transform the original problem into the one in a differentially flat space, and seek the equilibrium strategies for the transformed problem substitutionally. We further prove that the trajectory tracking error satisfies a probabilistic bound, whose confidence interval tightens as the decrease of noise variance during the interaction. Comparative simulation results show that our method outperforms the learning-based method in terms of robustness, parameters setting, and time consumption. Experiment results further show that the tracking error under the proposed human-robot cooperative algorithm is reduced by 55% compared to the human direct control.

Modeling of an Autonomous Electric Propulsion Barge for Future Inland Waterway Transport

International trade is continuously rising, leading to an increase in the flow of goods passing through transportation hubs, including air and sea. In addition, the aging fleet of inland vessels necessitates renewal through the construction of new vessels, presenting opportunities for the adoption of modern transport technologies. Autonomous barges can transport bulk and containerized cargo between the central port of a specific region and smaller satellite ports, enabling the dispersal of goods over a wider area. Equipping autonomous barges with advanced sensors, such as LIDAR, computer vision systems that operate in visible light and thermal infrared, and incorporating advanced path finding and cooperation algorithms may enable them to operate autonomously, subject only to remote supervision. The purpose of this study is to explore the potential of autonomous electric propulsion barges in inland waterway transport. Given the increasing demand for efficient and sustainable transport solutions as a result of various new policies, which have set new ambitious goals in clean transportation, this study aims to develop a proposition of an electric propulsion hybrid drive inland waterway barge, and compare it to a conventional diesel-powered barge. The methodology involves the creation of a simulation model of an inland waterway class IV electric barge, equipped with advanced sensors and autonomous control systems. The barge’s navigation is managed through a multi-agent system, with evolutionary algorithms determining a safe passage route. This research also utilizes a proprietary networked ship traffic simulator, based on real inland vessel recorded routes, to conduct the autonomous navigation study. The energy consumption of the barge on a route resulting from the ship traffic simulation is then examined using the mathematical model using the OpenModelica package. As a result of the study, the proposed hybrid propulsion system achieved a 16% reduction in fuel consumption and CO2 emissions, while cutting engine operation time by more than 71%. The findings could provide valuable insights into the feasibility and efficiency of autonomous electric propulsion barges, potentially helping future developments in inland waterway transport.

Synergy of the Triad “Education - Employer - Labor Market” as a Strategy for Advanced Forecasting of Social Order

Key words: market environment, reform, education system, social partnership, social order, corporate nature, labor market demands The article is devoted to new strategies for the interaction of the corporate triad “education - employer - labor market”. Nowadays, the orientation of education towards fundamental knowledge has eventuated to be out of demand in the labor market, since the real economy demands specialists with experience and skills in the application of this knowledge. Education faces a new challenge: strengthening the social partnership between universities and the labor market in order to obtain synergistic results. In this triad, the employer acquires a special role, since it is the one who is the final consumer of educational services. In fact, education and the labor market should integrate their efforts in the interest of a common goal: the sustainable development of countries and social welfare. Education and science play a key role in this integration, as they are generators of innovative development, new knowledge, and technologies for the application of that knowledge. The article describes international experiences of strategic partnership between universities and business communities, so it suggests potential algorithms for effective cooperation.

Coordinated Control of Quadrotor Suspension Systems Based on Consistency Theory

This paper designs a cooperative control method for the multi-quadrotor suspension system based on consistency theory and realizes the cooperative formation trajectory tracking control of the multi-quadrotor suspension system by designing a consistent formation cooperative algorithm of virtual piloting and a nonlinear controller. First, a new quadrotor suspension system model is established based on the traditional quadrotor model using the Newton–Euler method. This model can accurately reflect the influence of the load on the quadrotor while obtaining the swing of the load. Then, the vertical and horizontal positions are designed separately based on the quadrotor motion characteristics, and the formation algorithm based on the virtual pilot consistency theory ensures that the final convergence of each position is consistent. An integral backstepping controller and an integral backstepping sliding mode controller are designed for quadrotor position, attitude, and load swing control to achieve accurate and fast quadrotor trajectory tracking control while reducing load swing. The stability of all the controllers is demonstrated using Lyapunov functions. Finally, a multi-quadrotor suspension system formation cooperative simulation experiment is designed to verify the designed control method.

Scheduling operations in a large hospital by multiple agents

The scheduling of operations in a large hospital is performed jointly by several groups of people, each with its own objective and constraints. It is a two-phase process, starting with the allocation of operating rooms to wards, and followed by the scheduling of operations in each operating room of the hospital on each day. The final schedule must satisfy all inter-ward hard constraints, such as the allocation of anesthetists, nurses, and equipment to operations that are taking place in parallel, and ideally, it should also address soft constraints such as taking the urgency and complexity of operations into consideration.This study contributes to the ongoing effort of adapting multi-agent optimization models and algorithms to real-world applications by modeling the problems in both phases as distributed constraint optimization problems (DCOPs), with different properties. The first phase includes partially cooperative ward-representing agents, allocating operating rooms for daily usage among themselves. In the second phase, ward-representing agents interact with agents representing constraining elements, in order to generate daily operation schedules for each operating room, thus forming a unique bipartite constraint graph. On one side are the ward representatives, while on the other are the agents representing the constraining resources. Each agent has a non-trivial local problem to solve, and its solution serves as the proposed assignment in the distributed algorithm.The study begins by discussing the properties required of the algorithms needed to solve the two phases. It then proposes adjustments to existing distributed partially cooperative algorithms and local search algorithms to solve these problems, and compares the results of different variants of these algorithms. The results obtained for both phases emphasize that successful collaboration is predicated on two requirements: that agents hold consistent information regarding their peers’ states and that the degree of exploration undertaken by the algorithm is restricted in order to produce high-quality solutions.

Research on Computer Aided Laboratory Based on Cross-Multiplier Multi-Data Flow Collaborative Algorithm

<p>The paper develops a set of mobile laboratories based on the grid data cloud platform. The laboratory proposed a multi-data flow cooperative algorithm based on a cross-bus four-layer temporal space model and a cross-directional multiplier. This algorithm achieves the purpose of updating most data streams as a whole. The system establishes a statistical analysis system of data flow from multiple perspectives and mines and monitors multiple data flows. The paper divides most data streams into several linear modules, and corresponding matrices are formed. Finally, the simulated test results of the paper show that the CPU usage of the mobile laboratory computer-aided system based on the power network is very small. The system processing efficiency is high.</p> <p> </p>

Evolutionary reinforcement learning algorithm for large-scale multi-agent cooperation and confrontation applications

Evolutionary reinforcement learning algorithm for large-scale multi-agent cooperation and confrontation applications

A Blockchain-Based Cooperative Authentication Mechanism for Smart Grid

With the advancement of smart devices, the operation and communication of smart grids have become increasingly efficient. Many smart devices such as smart meters, smart transformers, and smart grid controllers are already widely used in smart grids. Thus, a series of complex architectures and a series of communication modes have been formed. However, these smart devices will be exposed to various cyber attacks such as distributed denial of service (DDoS) attack and replay attack. This is because they are open and dynamic. Therefore, there are serious security problems in the complex architectures and the communication modes. In this paper, we propose a multi-domain authentication mechanism based on blockchain cooperation to maintain the security of smart devices. In this mechanism, we propose a series of methods and algorithms, which include initialization method based on blockchain cooperative authentication, dynamic change method of intelligent devices and information, cross-domain authentication algorithm, and cross-domain key cooperative algorithm. To demonstrate the security and effectiveness of our proposed mechanism, we analysed its security and conducted a series of simulation experiments. The analysis and simulation experiments show that our proposed approach is secure and effective.

Stability and Distributed Optimization for AC Microgrid Considering Line Losses and Time Delay

With the development of distributed renewable generations, a large number of distributed generations (DGs) are connected to the microgrid. Therefore, distributed cooperative algorithms are more suitable for optimal dispatch of the microgrid than centralized algorithms. This paper proposes a novel distributed cooperative control method for optimal dispatch of microgrids, considering line losses and time delay. First, the optimization model of the microgrid considering line losses is established, and optimality conditions are obtained. Second, a novel distributed optimization method considering line losses is proposed, where the primary control is to achieve optimal dispatch, and the secondary control is to achieve frequency regulation. Third, the stability of the system under time delay is analyzed, and the robust stability conditions are obtained. Finally, simulation results verify the the effectiveness of the proposed method.