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In recent years, distributed optimization problem have a wide range of applications in various fields. This paper considers the prescribed-time distributed optimization problem with/without constraints. Firstly, we assume the state of each agent is constrained, and the prescribed-time distributed optimization algorithm with constraints is designed on the basis of gradient projection algorithm and consensus algorithm. Secondly, the constrained distributed optimization problem is transformed into the unconstrained distributed optimization problem, and according to the gradient descent algorithm and consensus algorithm, we also propose the prescribed-time distributed optimization algorithm without constraints. By designing the appropriate objective functions, we prove the multi-agent system can converge to the optimal solution within any prescribed-time, and the convergence time is fully independent of the initial conditions and system parameters. Finally, three simulation examples are provided to verify the validity of the designed algorithms. • For distributed optimization problem with convex constraints, we design an effective prescribed-time distributed algorithm which can guarantee the multi-agent system converge to the optimal solution. • The designed algorithm in this paper can achieve convergence within any prescribed-time, and the convergence time is fully independent of the initial conditions and system parameters. • In this paper, we not only consider the condition that the agents' states are constrained, but also ensure that the multi-agent system can reach the optimal solution within any prescribed-time, which is more practical. [ABSTRACT FROM AUTHOR]

Improving generalization ability in multi-robot formation can reduce repetitive training and calculation. In this paper, we study the multi-robot formation problem with the ability to generalize the target position. Since the generalization ability of neural network is directly proportional to spatial dimension, we adopt the strategy of using different networks to solve different objectives, so that the network learning can focus on the learning of one objective to obtain better performance. In addition, this paper presents a distributed deep reinforcement learning method based on soft actor–critic algorithm for solving multi-robot formation problem. At the same time, the formation evaluation assignment function is designed to adapt to distributed training. Compared with the original algorithm, the improved algorithm can get higher reward cumulative values. The experimental results show that the proposed algorithm can better maintain the desired formation in the moving process, and the rotation design in the reward function makes the multi-robot system have better flexibility in formation. The comparison of control signal curve shows that the proposed algorithm is more stable. At the end of the experiments, the universality of the proposed algorithm in formation maintenance and formation variations is demonstrated. [ABSTRACT FROM AUTHOR]

Assignment of one of two possible directions to every edge of an undirected graph G = (V , E) is called an orientation of G. The resulting directed graph is denoted by G ⃗. A strong orientation is one in which every vertex is reachable from every other vertex via a directed path in G ⃗. The diameter of G ⃗ , i.e., the maximum distance from any vertex to any other vertex, depends on the particular orientation. The minimum diameter among all possible orientations of G is called the oriented diameter diam ⃗ (G) of G. Let n , k be two integers such that 1 ≤ k < n. In the realm of interconnection networks of processing elements, an (n , k) -star graph S n , k offers a topology that permits to circumvent the lack of scalability of n -star graphs S n. The oriented diameter quantifies an upper limit on the delay in communication over interconnection networks. In this paper, we present a strong orientation scheme for S n , k that combines approaches suggested by Cheng and Lipman (2002) for S n , k with the one proposed by Fujita (2013) for S n , reaping benefits from both worlds. Next, we propose a distributed routing algorithm for S n , k ⃗ inspired by an algorithm proposed in Kumar et al. (2021) for S n ⃗. With the aid of both the orientation scheme and the routing algorithm, we show that diam ⃗ (S n , k) ≤ ⌊ n + k 2 ⌋ + 2 k + 6 − δ (n , k) where δ (n , k) is a non-negative function. The function δ (n , k) takes on values 2 k − n , 0 , and n − 3 k 2 respectively for three disjoint intervals k > n 2 , n 3 < k ≤ n 2 and k ≤ n 3 . For every value of n , k , our upper bound performs better than all known bounds in literature. [ABSTRACT FROM AUTHOR]

This paper designs a consensus control protocol for multi-agent systems(MAS) based on the edge-event-triggering mechanism to achieve a leader-following consensus under Denial-of-service (DoS) attacks. First, the dynamics model of the multi-agent system is given, and the DoS attack is modeled. A compensator is designed for each follower separately to keep tracking the state of the leader. In this paper, agents are classified into leaders, informed followers, and uninformed followers. The event-triggering conditions are designed separately. Then, the controller of each agent is designed using the difference between the state of the compensator and the agent state. Using the Lyapunov stability theory, the effectiveness of the designed control strategy under the DoS attack is analyzed, and the constraints on the control parameters are given. Additionally, we proved that the proposed control strategy could avoid the Zeno behavior. Finally, the effectiveness of the proposed control strategy is verified through numerical simulations. • Designed the follower adaptive compensators without global information. • Designed the edge-event-triggering conditions for each type of agent. • Employed state estimations to maximize communication resources when an edge is under attack. • Zeno behavior is avoided by ensuring that triggering events occur at discrete intervals. [ABSTRACT FROM AUTHOR]

The problem of robust distributed estimation over dynamic and streaming graph signals is investigated in this paper. Existing works related to distributed estimation over dynamic and streaming graph signals are mainly derived from the Mean-Square-Error criterion, and they are vulnerable to non-Gaussian noise. Therefore, a new kind of diffusion Mixture correntropy (d-MC) algorithm is developed to deal with the disadvantage in this paper. Incorporating the diffusion strategy and a novel cost function, the proposed algorithm could accurately estimate the graph filter parameter with the dynamic and streaming graph signals, and achieve desirable performance under both Gaussian and impulsive noise environment. Besides, the theoretical analysis results of mean and mean-square stability are derived. Simulations on various case studies indicate the desirable performance of proposed d-MC algorithm by comparing it to other benchmarks. • Distributed estimation over dynamic streaming graph signals is considered. • The robust problem is also studied facing Gaussian and impulsive noise. • A diffusion Mixture correntropy (d-MC) algorithm is designed. • The theoretical analysis is provided to verify the stability. • Experimental results are carried out to show the desirable performance. [ABSTRACT FROM AUTHOR]

An emerging time-varying distributed multi-energy management problem (MEMP) considering time-varying load and emission limitations for resisting time-varying external disturbances and communication time delays in the multi-microgrid (MMG) system is investigated. Each microgrid (MG) contains some smaller microgrids (SMGs), which are connected by energy routers (ERs) of the system and can monitor energy in real-time with each other. In addition, a time-varying multi-energy management optimization model (MEMOM) is proposed in this paper in order to minimize the total cost of the MEMP which considers environmental cost, renewable energy cost and fuel cost. Furthermore, time-varying distributed neurodynamic optimization algorithms are proposed for solving the above MEMP based on consensus theory and sliding mode control technique. Compared with the optimization algorithms which consist of symbolic functions proposed in traditional energy management problems, algorithms consisting of hyperbolic tangent functions proposed in this paper can effectively reduce the oscillation of the algorithms and improve the stability of algorithms. Furthermore, the algorithm can converge the optimal trajectory of optimization problems with time-varying external disturbances and communication time delays. Meanwhile, the stability and convergence of the algorithms are proved theoretically by constructing appropriate Lyapunov functions. Finally, the performance evaluation results of numerical simulations show that the proposed algorithms can efficiently handle energy trading under time-varying load and maintain excellent stability with time-varying external disturbances and communication time delays. • This paper proposes a time-varying MEMOM in MMG scenario for the distributed MEMP, which can achieve optimal energy management based on fuel, renewable energy and time-varying load. Meanwhile, ERs in the model can monitor the energy balance under the SMGs in real-time. • Compared with the traditional time-invariant smart grid model, the time-varying MEMOM proposed in this paper contains not only the time-varying load but also the time-varying emission limitations and time-varying objective functions. • The fully distributed neurodynamic algorithms with hyperbolic tangent functions are proposed in this paper. Compared with classic distributed algorithms designed with symbolic functions in [1,2] , the continuous hyperbolic tangent function is more conducive to reduce the oscillation of algorithms and improve the stability of algorithms. In addition, the algorithms can effectively resist external time-varying disturbances and communication time delays. [ABSTRACT FROM AUTHOR]

This paper investigates the controllability of impulsive systems with input delay and impulse delay and its applications in multi-agent networks. We adopt the geometric and algebraic analytical tools to establish some easily verified controllability conditions for the considered system model. First, the analytic solution of the considered system is established on every impulsive interval by using ordinary differential equation theory. Then, according to the solution derived, some sufficient complete controllability criteria are developed to reveal the role of the Gramian matrices in different subintervals. By introducing a row matrix of different kinds of Gramian matrices, a complete controllability condition that is proved to be necessary and sufficient is further obtained. By using the relevant geometric matrix theory, the derived algebraic controllability condition is then converted to a geometric one. On other hand, we introduce a multi-agent network with delayed input and impulse and investigate its controllability. By resorting to graph theory and matrix theory, several factors affecting the controllability of the considered multi-agent networks are investigated, such as the topology structure, the inner coupling matrix, and the dynamics of each agent. Finally, two numerical examples are worked out to verify the derived controllability criteria. • Controllability criteria relying on impulse and delay are established. • A Gramian-like controllability condition with less conservativeness is obtained. • Controllability of impulsive delayed multi-agent network is first studied. • Controllability conditions related to the network factors and dynamics are revealed. [ABSTRACT FROM AUTHOR]

This paper studies a few randomized algorithms (e.g., random walks, gossip) in peer-to-peer networks. We leverage the Docker virtual container technology to develop implementations of the peer-to-peer networks and of these distributed randomized algorithms executing on top of them. We can thus analyze their behavior and performance in realistic settings. We further consider the problem of identifying high-risk bottleneck links in the network with the objective of improving the resilience and reliability of peer-to-peer networks. We propose a randomized algorithm to solve this problem and evaluate its performance by simulations. [ABSTRACT FROM AUTHOR]

In order to improve the multiple-missile cooperative attack capability and penetration capability, this paper investigates two three-dimensional impact-angle-constrained cooperative guidance strategies against maneuvering target for controllable thrust missiles. First, a three-dimensional nonlinear guidance model is established that does not assume small missile lead angles in the guidance process. Second, in the line-of-sight (LOS) direction of the cluster cooperative guidance strategy, the proposed guidance algorithm transforms the simultaneous attack problem into a second-order multiagent consensus problem, which effectively solves the practical problem of low guidance precision provoked by the time-to-go estimation. Then, by combining second-order sliding mode control (SMC) and nonsingular terminal SMC theory, the guidance algorithms in the normal and lateral directions to the LOS are designed, respectively, so that the multi-missile can accurately attack a maneuvering target while satisfying the impact angle constraints. Finally, by utilizing the second-order multiagent consensus tracking control in the leader-following cooperative guidance strategy, a novel leader-following time consistency algorithm is investigated to ensure that the leader and followers can attack the maneuvering target simultaneously. Moreover, the stability of the investigated guidance algorithms is proved mathematically. The effectiveness and superiority of the proposed cooperative guidance strategies are verified by numerical simulations. • This paper designs two three-dimensional impact-angle-constrained cooperative guidance strategies against maneuvering target. • This paper utilizes the relative distance and radial relative velocity as consensus variables to improve guidance precision. • This paper combines a nonsingular SMC with a second-order sliding mode control theory to improve guidance performance. • The proposed cooperative guidance algorithm has a simpler form and a more straightforward structure to avoid singularity problems. [ABSTRACT FROM AUTHOR]

This paper investigates the fixed-time distributed estimation problem for a class of second-order nonlinear systems with uncertain input, unknown nonlinearity and matched perturbation. A fixed-time distributed extended state observer (FxTDESO) consisting of a group of local observer nodes under directed communication topology is proposed, and each node can reconstruct both the full state and unknown dynamics of the system. To achieve fixed-time stability, a Lyapunov function is elaborated, and based on this, sufficient conditions for the existence of the FxTDESO are established. Under time-invariant and time-varying disturbance, the observation errors can converge to the origin and a small region of the origin within a fixed time, respectively, where the upper bound of the settling time (UBST) is irrelevant to the initial conditions. Compared to the existing fixed-time distributed observers, the proposed observer can reconstruct both the unknown states and uncertain dynamics, and only the output of the leader and 1-dimensional output estimates from the neighboring nodes are needed in the observer design which effectively reduces the communication load. The paper also extends previous finite-time distributed extended state observer to the case of time-variant disturbance and eliminates the complex linear matrix equation assumption that guarantees the finite-time stability. Furthermore, the FxTDESO design for a class of high-order nonlinear systems is also discussed. Finally, simulation examples are conducted to demonstrate the effectiveness of the proposed observer. • A fixed-time distributed extended state observer (FxTDESO) containing a group of local observers is developed for an uncertain second-order nonlinear system. Each local observer can accurately reconstruct both the full state and lumped uncertainties in a fixed time. Compared to previous distributed extended state observers, the upper bound of the settling time of the observation errors is regardless of the initial conditions. • Unlike the existing fixed-time distributed observers which can only be used to estimate the system states and require the full state of the leader and full-state estimates from the observer nodes, both the states and uncertain dynamics can be reconstructed by our observer. Moreover, only output information of the leader and 1-dimensional output estimates of the local observer nodes are needed, which can effectively reduce the communication load. • When only the output information is available, it is challenging to find a way to analyze the fixed-time stability. To this end, a Lyapunov function is elaborated, and based on this, sufficient conditions for realizing fixed-time stability of the FxTDESO under time-invariant and time-varying disturbance are established, respectively. Furthermore, the FxTDESO design and stability analysis for a class of high-order nonlinear systems is briefly discussed. [ABSTRACT FROM AUTHOR]

This paper mainly investigates the exponential consensus problem for the multi-agent systems (MASs) with nonlinear discontinuous dynamics and time-varying delay. A novel aperiodically intermittent distributed control strategy is proposed to force the state of each agent to the common trajectory, where the configuration of control width and rest width can be aperiodic. Meanwhile, in order to limit the control effects into certain reasonable ranges, an improved saturation algorithm is proposed, which effectively reduces the non-smoothness of the control signal. Sufficient conditions for the exponential consensus on the discontinuous MASs are obtained through the Filippov differential inclusion (FDI), the Lie derivative method (LDM) and the measurement selection theorem (MST). Finally, the validity and feasibility of the main theories is demonstrated by numerical simulations. • The consensus on multi-agent systems with discontinuous nonlinear dynamics and time-varying delay is considered in this paper by introducing the Filippov regularization process. • This paper puts forward a novel aperiodically intermittent distributed control strategy, which simulates the realistic situations better and extends the application ranges of the intermittent control well. • An improved saturation strategy is designed by combining the Gaussian error function and the differential mean value theorem in order to limit the control effects into certain reasonable ranges. • By applying the Filippov differential inclusion theorem and the measurement selection theorem, sufficient conditions for the exponential consensus on the discontinuous multi-agent systems are obtained. [ABSTRACT FROM AUTHOR]

Minimizing energy consumption is one of the most interesting issues in recent industrial production. Energy-efficient scheduling is a promising approach to reducing energy consumption in manufacturing systems. However, energy-efficient scheduling is a problem of great interest, especially for the Distributed permutation Flow-Shop Manufacturing Problem (DPFSMP). This paper addresses the critical challenge of energy efficiency in the industry of DPFSMP optimization, where task processing times depend closely on their position in the production line. The objective is to minimize the total energy consumption (TEC) for all machines in distributed workshops. To solve this problem, we propose two efficient constructive algorithms for energy-efficient scheduling of this type of problem with speed-dependent tasks: the Nawaz Enscore and Ham (NEH) based algorithm and the Greedy Random Adaptive Search Procedure (GRASP) based algorithm. We evaluate the performance of the two proposed algorithms on a set of benchmark problems to identify the one with the best performance. The results show that the (NEH) algorithm outperforms the GRASP algorithm in terms of energy efficiency and solution quality. In fact, the proposed algorithms can be used by manufacturers to reduce energy consumption in their production systems. This can help manufacturers save money, reduce environmental impact and improve overall sustainability. [ABSTRACT FROM AUTHOR]

This article deals with the problem of distributed event-triggered tracking control in mobile sensor networks (MSNs) with a jointly connected topology (JCT). Two schemes are proposed for linear and Lipschitz nonlinear MSNs to estimate and track a mobile target. The proposed schemes are established using an event-triggered method to avoid continuous exchange of information between sensor nodes. In comparison with the other research under event-triggered communication strategies where states of the target are available, this paper considers that the states of the target are not available and two event-triggered algorithms are established for sensor nodes to estimate and follow the states of the continuous-time targets that can be seen in various real-world applications. Also, the proposed schemes are designed for the JCT with disconnected graphs which means the communication topology of the MSN is not required to be connected for all time instants. By employing the Cauchy convergence criterion and a common Lyapunov function, sufficient conditions are also established to ensure event-based tracking control subject to JCT. The effectiveness of the proposed work is verified by presenting simulation examples. • Two new event-triggered tracking control (ETC) schemes for mobile sensor networks. • The ETC is obtained without requiring continuous communication among sensor nodes. • The proposed schemes can be applied to networks with jointly connected topology. • The stability of the schemes is also analyzed. [ABSTRACT FROM AUTHOR]

Hydrodynamic models with rain-on-the-grid capabilities are usually computationally expensive for automatic parameter estimation. In this paper, we present a global optimization-based algorithm to calibrate a fully distributed hydrologic-hydrodynamic and water quality model (HydroPol2D) using observed data (i.e., discharge, or pollutant concentration) as input. The algorithm finds near-optimal set of parameters to explain observed gauged data. This framework, although applied in a poorly-gauged urban catchment, is adapted for catchments with more detailed observations. The results of the automatic calibration indicate NSE = 0.99 for the V-Tilted catchment, RMSE = 830 mg L-1 for salt concentration pollutograph in a wooden-plane (i.e., 8.3% of the event mean concentration), and NSE = 0.89 in a urban real-world catchment. This paper also explores the issue of equifinality (i.e., multiple parameters giving the same calibration performance) in model calibration indicating the performance variation of calibrating only with an outlet gauge or with multiple gauges within the catchment. [Display omitted] • An automatic calibration algorithm for distributed flood and water quality modeling is developed. • It uses HydroPol2D model and calibrate water quantity and quality parameters globally. • Data from observed gauges such as discharges, depths, and concentration is used for calibration. • Poorly placed gauges and low runoff events can increase equifinality during calibration. [ABSTRACT FROM AUTHOR]

Word Sense Disambiguation (WSD) is significant for improving the accuracy of the interpretation of a Natural language text. Various supervised learning-based models and knowledge-based models have been developed in the literature for WSD of the language text. However, these models do not provide good results for low-resource languages, due to the lack of labelled and tagged data. Therefore, in this work, we have examined different word embedding techniques for word sense disambiguation of the Hindi language texts. Several studies in the literature show that these embeddings have been utilized for different foreign languages in the field of word sense disambiguation. However, to the best of our knowledge, no such work exists for the Hindi language. Therefore, in this paper, we utilize various existing word embeddings for WSD of Hindi text. Moreover, we have created Hindi word embeddings on articles taken from Wikipedia and test the quality of the created word embeddings using Pearson correlation. In this direction, we perform different experiments and observe that Word2Vec model gives best performance among all the considered embeddings on the used Hindi dataset. In our method, the proposed model directly takes input that is trained with word embedding methods and helps to develop a sense inventory using clustering that has been employed for performing disambiguation. Experimental observations indicate that the performance of the proposed approach is moderate and competent in terms of accuracy. The paper, thus, presents how WSD can leverage these representations to encode rich semantic information. [ABSTRACT FROM AUTHOR]

The distributed online convex optimization problem with time-varying constraints for multi-agent networks is addressed in this article. The purpose is to optimize a sequence of time-varying global cost functions defined as the accumulated values of local cost functions, also attempt to meet the requirement of a sequence of time-varying coupled constraint functions which denote the sum of local constraint functions. Cost functions and constraint functions are unknown to agents beforehand. It is supposed that each agent in the network communicates with its neighbours through a uniformly strongly connected sequence of time-varying directed communication topologies. This paper proposes the bandit distributed primal-dual mirror descent push-sum (BDPDMDPS) algorithm constructed by bandit primal-dual, mirror descent and push-sum methods. Operational performance of the presented algorithm is measured by expected regret and expected constraint violation, both of which are proved to be sublinear with respect to the total iteration span T in this paper. Finally, a numerical simulation example is shown, which confirms the results for expected regret and expected constraint violation of BDPDMDPS algorithm. [ABSTRACT FROM AUTHOR]

This paper presents a distributed trajectory optimization algorithm for the reconfiguration of large-scale spacecraft formation, which uses a low thrust propulsion system to safely guide the spacecraft formation to a desired formation. Formation reconfiguration is formulated as a trajectory optimization problem with complex constraints, and the relative motion is accurately described by a nonlinear relative dynamics considering J 2 perturbation. The collision avoidance constraint is convexified into a tangent plane to ensure the accuracy of the solution. The resulting nonlinear trajectory optimization problem is solved by applying the h p -adaptive pseudospectral method to convert it into a nonlinear programming problem. In order to overcome the disadvantage of huge computation of centralized algorithm considering collision avoidance, "predicted trajectory" is introduced to transmit information between spacecraft, and the parallel computing is implemented. Finally, a numerical simulation is given to verify the computational efficiency and collision avoidance effectiveness of the proposed distributed algorithm. • Distributed trajectory optimization algorithm for massive formation reconfiguration. • The J 2 nonlinear relative dynamics is used to obtain accurate optimal trajectory. • Parallel computation is achieved by information interaction of neighbors. • Neighbor updating is proposed to reduce communication traffic. • Significantly decrease computation and achieve collision avoidance. [ABSTRACT FROM AUTHOR]

This paper considers a class of multi-agent distributed convex optimization with a common set of constraints and provides several continuous-time neurodynamic approaches. In problem transformation, l 1 and l 2 penalty methods are used respectively to cast the linear consensus constraint into the objective function, which avoids introducing auxiliary variables and only involves information exchange among primal variables in the process of solving the problem. For nonsmooth cost functions, two differential inclusions with projection operator are proposed. Without convexity of the differential inclusions, the asymptotic behavior and convergence properties are explored. For smooth cost functions, by harnessing the smoothness of l 2 penalty function, finite- and fixed-time convergent algorithms are provided via a specifically designed average consensus estimator. Finally, several numerical examples in the multi-agent simulation environment are conducted to illustrate the effectiveness of the proposed neurodynamic approaches. [ABSTRACT FROM AUTHOR]

Consider a team of k ≤ n autonomous mobile robots initially placed at a node of an arbitrary graph G with n nodes. The dispersion problem asks for a distributed algorithm that allows the robots to reach a configuration in which each robot is at a distinct node of the graph. If the robots are anonymous, i.e., they do not have any unique identifiers, then the problem is not solvable by any deterministic algorithm. However, the problem can be solved even by anonymous robots if each robot is given access to a fair coin which they can use to generate random bits. In this setting, it is known that the robots require Ω (log Δ) bits of memory to achieve dispersion, where Δ is the maximum degree of G. On the other hand, the best known memory upper bound is m i n { Δ , m a x { log Δ , log D } } (D = diameter of G), which can be ω (log Δ) , depending on the values of Δ and D. In this paper, we close this gap by presenting an optimal algorithm requiring O (log Δ) bits of memory. [ABSTRACT FROM AUTHOR]

This paper is devoted to the topology-based dynamic event-triggered leader-following consensus issue of general linear multi-agent systems under switching topological structures. The novel topology-based distributed dynamic event-triggered rule is formed, in which a topology-based dynamic auxiliary variable is introduced to reduce the conservatism and save resources. Under novel triggering rules, topology-based distributed event-triggered controllers are constructed. Different control gains are designed for different topological structures. Compared with existing works, conservatism is further reduced. Then the leader-following consensus is achieved with a global exponential convergence rate. Moreover, the average dwell time method is adopted to relax restrictions on the switching speed of topological structures and an original analytical thought is put forward to rule out the Zeno phenomenon concisely and clearly. Finally, two numerical examples and a realistic mass–spring system are utilized to verify the feasibility and superiority of the proposed theory. • The novel topology-based dynamic event-triggered rules are put forward. • The topology-based distributed control laws are proposed. • A novel lemma is first proposed to rule out the Zeno phenomenon concisely. • The limit on the switching speed of topological structures is relaxed. [ABSTRACT FROM AUTHOR]

This paper proposes a sensor network information fusion based distributed state estimation algorithm for tracking non-cooperative maneuvering target. In order to lower the communication burden and energy consumption, an event-triggered mechanism is introduced. The distributed state estimation algorithm for maneuvering target tracking is designed by two stages, namely, local filtering stage and consensus fusion stage. The algorithm proposed in this paper can be used to obtain the high accurate state estimation of the target by introducing a multiple suboptimal fading factor even when the target makes big maneuvering. Moreover, a contribution factor is designed in the information fusion stage to improve the accuracy of maneuvering target state estimation and reduce the consensus iteration times. Besides, the stochastic boundedness of the event-triggered distributed estimation algorithm is proved by introducing a stochastic process. Finally, Monte Carlo numerical simulation example is designed to illustrate the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

A concurrent system specified as a rewrite theory can be analyzed symbolically using narrowing-based reachability analysis. Narrowing-based approaches have been applied to formally analyze cryptographic protocols and parameterized protocols. However, existing narrowing-based analysis methods, based on a breadth-first-search strategy, cannot deal with generic distributed systems with objects and messages due to the symbolic state-space explosion problem and implicit constraints imposed on object-oriented systems. This paper proposes a heuristic search approach for narrowing-based reachability analysis to guide the search for counterexamples with a small number of objects. As a result, our method can effectively find a counterexample if an error state is reachable. In addition, this paper also shows how to encode implicit object-oriented constraints using order-sorted signatures and equational constraints. We demonstrate the effectiveness of our technique using a nontrivial distributed consensus algorithm. [ABSTRACT FROM AUTHOR]

• This paper takes the community electricity trading market with multiple prosumers participating at the distribution network level as the research object, fully considering the impact of the psychological gap effect of prosumers on system operation from a micro-perspective, and proposes a distributed electricity market trading mechanism based on the Stackelberg game, which in order to provide new ideas for the improvement and optimization of the distribution-side electricity market trading system, and the core findings of the paper are as follows:. • The trading mechanism proposed in this paper could better schedule translatable loads and energy storage units, reduce the fluctuation amplitude of the load curve while performing peak cutting and valley filling, stimulate the flexibility of user-side resources, and ensure the stability of system operation. • The trading mechanism proposed in this paper could obtain more evidence-based and reasonable prices for purchasing and selling electricity within the region, ensuring that the distribution network operator could obtain considerable profits while effectively improving the profits of prosumers, which is conducive to improving the economic efficiency of system operation. • In terms of the impact of the gap effect on the energy utility and profits of prosumers, the larger the gap effect coefficient (prosumers are more sensitive to expected deviations), the lower their electricity utility and overall profits, and when the individual electricity utility and overall profits of prosumers are too low, they may withdraw from market transactions. Therefore, government departments could implement policies, such as providing subsidies and adjusting taxes, to further stimulate the enthusiasm of prosumers to participate in distributed market transactions. With the large-scale customer-side development of distributed energy and continuous advancements in power market reform, the market-oriented trading of distributed power generation with the participation of prosumers has gradually become an important solution to promote the consumption of distributed energy. In addition, prosumers exhibit obvious bounded rationality characteristics; therefore, the design of efficient and reasonable trading mechanisms determines the efficiency of the market. From this perspective, a distributed electricity market trading mechanism based on the Stackelberg game is proposed for the community electricity trading market, with multiple prosumers participating in the distribution side. Firstly, the trading architecture of the community electricity market was established under the distribution network level. Secondly, the pricing strategy of the distribution network operator and the quantitative strategy of community prosumers were analysed; on this basis, the characteristics of the bounded rationality of prosumers were considered, and the impact of the psychological gap effect of prosumers on their energy utility was introduced into the objective function of prosumers. This facilitated the construction of a Stackelberg game model in which distribution network operators lead and community prosumers follow. Thirdly, the Stackelberg equilibrium was proven to exist and be unique, which was then solved by the distributed algorithm – combining the evolutionary algorithm and quadratic programming – which improves the solution efficiency and effectively protects the privacy of prosumers. Finally, the effectiveness and feasibility of the proposed trading mechanism were verified through numerical examples. [ABSTRACT FROM AUTHOR]

This paper is concerned with a conflict that N -pursuers versus M -evaders. It is a multi-pursuer multi-evader game extended from classical differential game theory to simultaneously address target selection and multi-player pursuit-evasion. Every pursuer attempts to intercept the evader it has chosen as its target while every evader does the opposite. This is modeled as a multi-player nonzero-sum differential game under a weighted directed graph. Moreover, a novel and more practical framework, the Unique-Choice (UC) games framework, is provided. Hamilton–Jacobi–Isaacs (HJI) is used for solving the game to derive the optimal distributed control strategy for each player based on graph theory. An important but has not been extensively studied issue, target selection, is studied. Different from the former research, this paper proposes a more reasonable criterion for target selection which makes the cost of pursuers minimized. The pursuers are proven to intercept evaders successfully with the proposed optimal pursuit strategy. Several illustrative examples under two different cases are presented, and the results show that the cost of pursuers with the proposed target selection algorithm is the minimum of all results. [ABSTRACT FROM AUTHOR]

This paper proposes a robust finite element Kalman filter for the state estimation of flexible space tether systems, which are critical for space exploration and satellite missions. Due to the limited access to placing sensors along the flexible tether to measure them directly, the measurement of the state of these systems is challenging. To address this, a fully expandable, scalable, and robust approach is developed that fuses position/velocity sensors at tether ends, distributed fiber optic strain measurement along the tether, and a position/velocity estimation by a finite element-based Kalman filter to estimate the state of the tether system. The proposed estimator is mathematically proven to be observable, stable, and robust. The effectiveness of the estimator is demonstrated through numerical simulations of a space tether system in orbit, and the method can be extended to any flexible body system. The results of the simulations show that the proposed approach can accurately estimate the state of the tether system, even in the presence of disturbances and noise. • Expandable, scalable FE-Kalman state estimator for flexible space tether systems. • Data fusion with tether end position/velocity, distributed fiber optic strain measure, and estimated internal position/velocity. • Mathematically proven observability, stability, and robustness to noise of the estimator. [ABSTRACT FROM AUTHOR]

A distributed optimization method for solving nonlinear equations with constraints is developed in this paper. The multiple constrained nonlinear equations are converted into an optimization problem and we solve it in a distributed manner. Due to the possible presence of nonconvexity, the converted optimization problem might be a nonconvex optimization problem. To this end, we propose a multi-agent system based on an augmented Lagrangian function and prove that it converges to a locally optimal solution to an optimization problem in the presence of nonconvexity. In addition, a collaborative neurodynamic optimization method is adopted to obtain a globally optimal solution. Three numerical examples are elaborated to illustrate the effectiveness of the main results. [ABSTRACT FROM AUTHOR]

With the rapid development of mobile communications, the dense deployment of small base stations (SBSs) in heterogeneous wireless networks (HW-Nets) has met the explosive growth in demand from terminals. However, due to the irregular deployment of SBSs, users frequently handover base stations in pursuit of high-quality services, which inevitably leads to load imbalance among SBSs. To alleviate load imbalance in HW-Nets and improve the efficiency of seamless handover, this paper proposes a distributed auction algorithm scheme based on dual active protocol stack (DAPS), which transforms the wireless access selection of terminals into the terminal-base station association process during movement to globally optimize network resource utilization. At the same time, this paper constructs a terminal-base station association graph and uses auction algorithms to solve the maximum weighted matching of the graph. The DAPS-based scheme can effectively avoid the problem of excessively long outage time caused by handovering outages. Simulation results show that compared with general distributed algorithms, the distributed auction algorithm based on DAPS can effectively balance the load of base stations and reduce the mobile outage latency by 59%. [ABSTRACT FROM AUTHOR]

The electric power distribution system (PDS) and the water distribution system (WDS) are coupled with each other through electricity-driven water facilities (EdWFs), such as pumps, water desalination plants, and wastewater treatment facilities. However, they are generally owned and operated by different utilities, and there does not exist an operator that possesses full information of both systems. As a result, centralized methods are not applicable for coordinating the operation of the two systems. This paper proposes a decentralized framework where the PDS and WDS operators solve their own operation problems, respectively, by sharing only limited information. Nevertheless, the boundary variables (i.e., the variables shared between two systems) are discontinuous due to their dependence on the on/off nature of EdWFs. Unfortunately, mature decentralized/distributed optimization algorithms like the alternating direction method of multipliers (ADMM) cannot guarantee convergence and optimality for a case like this. Therefore, this paper develops a novel algorithm that can guarantee convergence and optimality for the decentralized optimization of PDS and WDS based on a recently developed algorithm called the SD-GS-AL method. The SD-GS-AL method is a combination of the simplicial decomposition (SD), gauss–seidel (GS), and augmented Lagrangian (AL) methods, which can guarantee convergence and optimality for mixed-integer programs (MIPs) with continuous boundary variables. Nonetheless, the original SD-GS-AL algorithm does not work for the PDS-WDS coordination problem where the boundary variables are discontinuous. This paper modifies and improves the original SD-GS-AL algorithm by introducing update rules to discontinuous boundary variables (called the Auxiliary Variables Update step). The proposed mixed-integer boundary compatible (MIBC) SD-GS-AL algorithm has the following benefits: (1) it is capable of handling cases whose boundary variables are discontinuous with convergence and optimality guaranteed for mild assumptions, and (2) it only requires limited information exchange between PDS and WDS operators, which will help preserve the privacy of the two utilities and reduce the investment in building additional communication channels. Simulations on two coupled PDS and WDS test cases (Case 1: IEEE-13 node PDS and 11-node WDS, and Case 2: IEEE-37 node PDS and 36-node WDS) show that the proposed MIBC algorithm converges to the optimal solutions while the original SD-GS-AL does not converge for both test cases. The ADMM does not converge for the first test case while it converges to a sub-optimal solution, 63 % more than the optimal solution for the second test case. • We propose a decentralized optimization of the water-energy nexus. • The proposed algorithm handles mixed-integer boundary variables. • The optimality and convergence of the proposed algorithm are guaranteed. • Outperforms mature distributed algorithms like ADMM. [ABSTRACT FROM AUTHOR]

With the development of economic globalization, distributed hybrid flow shop scheduling problem (DHFSSP) has become prevalent in realistic manufacturing systems. Moreover, to accord with the actual production scenarios and satisfy the requirement of manufacturing market, it is imperative to comprehensively explore various complex manufacturing scenarios (e.g., production assembly) and production-constrained resources (e.g., worker resources) in DHFSSP. However, the integration mode of DHFSSP, assembly shop problem (ASP), and dual-resource constraints (DRC) has not been reported in existing literature. Thus, to fill out this research gap, this paper first attempts to investigate DAHFSSP-DRC with minimization the total tardiness (T T D). To solve this problem, a mixed-integer linear programming (MILP) model and a knowledge-based iterated greedy algorithm (KBIG) are presented. The novelties of KBIG are as follows: (1) An efficient decoding is developed to improve the solution's quality; (2) A knowledge-based NEH (KB-NEH) initialization strategy is presented to generate an initial solution; (3) A knowledge-based destruction and construction is designed to improve the exploration capability; (4) A product-based local search is proposed to enhance the exploitation capability. Additionally, to validate the proposed model, we implement CPLEX to solve it on 24 small-sized instances. To verify the effectiveness of the proposed KBIG, extensive experiments are conducted to compare with other 7 comparison algorithms on 405 large-sized instances. Experimental results illustrate that KBIG is superior to its competitors. • This paper first investigates DAHFSSP-DRC with minimization TTD. • A MILP model and a KBIG are proposed for DAHFSSP-DRC. • The importance level of different knowledge is considered in KBIG. • Experimental results prove the effectiveness of MILP model and KBIG. [ABSTRACT FROM AUTHOR]

The consortium blockchain has been widely applied in various fields such as agricultural product traceability, supply chain management, and logistics transportation. As an indispensable component of a consortium blockchain, the consensus algorithm ensures the consistency and trustworthiness of each node in the network. However, existing consensus algorithms in large-scale consortium blockchain scenarios suffer from low system throughput and high latency due to the complexity of communication processes, rendering them impractical for real-world use. To address these issues, this paper proposes a novel consensus algorithm called credit evaluation-based practical Byzantine fault tolerance (CE-PBFT). This algorithm designs a new node credit evaluation model that considers node completion rate, consensus decay, and node behavior. It effectively measures and reflects the specific reliability status of nodes during system operation, thereby enhancing system reliability and security. Additionally, the paper introduces the innovative use of decision tree algorithms to analyze network node behavior and simplifies the existing consensus protocol. Nodes are categorized as excellent, good, ordinary, or poor based on the classification results, and non-Byzantine nodes are dynamically selected accordingly. This greatly improves the overall efficiency of the system. The performance of CE-PBFT is validated through experiments and compared with PBFT, G-PBFT, RBFT, WBFT and PPoR. Experimental results demonstrate that in large-scale consortium scenarios, CE-PBFT significantly improves system throughput, effectively reduces transaction latency and communication overhead, and outperforms the compared protocols. [ABSTRACT FROM AUTHOR]

• Develop a generic framework for maintenance scheduling of multi-component systems with environment modeling, state representation and policy optimization. • Propose Guided-Probabilistic Reinforcement Learning for sampling-efficient maintenance scheduling by incorporating domain expert knowledge. • Develop Bayesian formulations to address data sparsity for environmental reward modeling, performance degradation, and exploration of action spaces. In recent years, multi-agent deep reinforcement learning has progressed rapidly as reflected by its increasing adoptions in industrial applications. This paper proposes a Guided Probabilistic Reinforcement Learning (Guided-PRL) model to tackle maintenance scheduling of multi-component systems in the presence of uncertainty with the goal of minimizing the overall life-cycle cost. The proposed Guided-PRL is deeply rooted in the Actor-Critic (AC) scheme. Since traditional AC falls short in sampling efficiency and suffers from getting stuck in local minima in the context of multi-agent reinforcement learning, it is thus challenging for the actor network to converge to a solution of desirable quality even when the critic network is properly configured. To address these issues, we develop a generic framework to facilitate effective training of the actor network, and the framework consists of environmental reward modeling, degradation formulation, state representation, and policy optimization. The convergence speed of the actor network is significantly improved with a guided sampling scheme for environment exploration by exploiting rules-based domain expert policies. To handle data scarcity, the environmental modeling and policy optimization are approximated with Bayesian models for effective uncertainty quantification. The Guided-PRL model is evaluated using the simulations of a 12-component system as well as GE90 and CFM56 engines. Compared with four alternative deep reinforcement learning schemes, the Guided-PRL lowers life-cycle cost by 34.92 % to 88.07 %. In comparison with rules-based expert policies, the Guided-PRL decreases the life-cycle cost by 23.26 % to 51.36 %. [ABSTRACT FROM AUTHOR]

Real-time algorithms have to adapt and adjust to new incoming patterns to provide timely and accurate responses. This paper presents a new distributed forecasting algorithm for streaming time series called StreamWNN. StreamWNN starts with an offline stage in which a forecasting model based on tuples of information fusion is created with historical data. In particular, this model consists of the fusion of patterns composed of past values of the time series with the future values of their k-nearest neighbors. Afterwards, streaming data starts to arrive. The model is incrementally updated in the online stage using a buffer with streaming data that more accurately matches the current model patterns. The model can be updated daily, monthly, quarterly or based on error thresholds. The methodology has been applied to Spanish electricity demand time series providing more accurate results when the model is updated incrementally. The best error results are obtained with the daily update of the model, resulting in an error between 2% and 3.5% depending on the prediction horizon. The model provides better error results than other algorithms. • StreamWNN is a real-time forecasting algorithm for time series received in streaming. • The prediction model is based on the fusion of relevant information related to the nearest neighbors. • Incremental learning deals with the change of distribution of streaming data. • StreamWNN predicts accurately Spanish electricity consumption in real-time. [ABSTRACT FROM AUTHOR]

For multi-attribute group decision-making (MAGDM) problems, this paper proposes a three-way consensus model based on regret theory (RT) under the framework of probabilistic linguistic term sets (PLTSs), i.e., the PL-RT-GTWD model. Specifically, the PL-RT-GTWD model mainly consists of the following two components: (1) The consensus measurement considering the relativity among decision-makers (DMs); (2) A three-way consensus feedback mechanism with the minimum adjustment. First, the relative relationship between DMs is supplemented, and the consensus degree of the DM evaluation matrix is measured by a newly developed distance measure. Second, an optimization model with the minimum adjustment for all DMs to modify and reach the final desired goal (group consensus opinions) is constructed, so that DMs can achieve a balance between reaching consensus and maintaining individual independence. Then, taking the minimum adjustment before and after the adjustment as the objective function, the increase of consensus degrees and the initial range of adjustment parameters as constraints, the goal programming on adjustment parameters is constructed. It is noteworthy that the initial range of adjustment parameters is divided via three-way decision (TWD) tools that consider the regret generated during the adjustment, so as to avoid the subjectivity of the adjustment and reduce adjustment costs. Finally, comparative and sensitivity analyses are carried out to verify the feasibility and superiority of the constructed model. • A three-way consensus model with regret theory is designed. • A new distance measure in PLTSs is defined. • A PL-RT-GTWD model is established. • A feedback adjustment mechanism is considered. • An optimization model with minimum cost is explored. [ABSTRACT FROM AUTHOR]