Effect of distribution of fines on evolution of cooperation in spatial public goods game

In the realm of public goods game,punishment,as a potent tool,stands out for fostering cooperation.While it effectively addresses the first-order free-rider problem,the associated costs can be substantial.Punishers incur expenses in imposing sanctions,while defectors face fines.Unfortunately,these monetary elements seemingly vanish into thin air,representing a loss to the system itself.However,by virtue of the redistribution of fines to cooperators and punishers,not only can we mitigate this loss,but the rewards for these cooperative individuals can be enhanced.Based upon this premise,this paper introduces a fine distribution mechanism to the traditional pool punishment model.Under identical parameter settings,by conducting a comparative experiment with the conventional punishment model,the paper aims to investigate the impact of fine distribution on the evolution of cooperation in spatial public goods game.The experimental results clearly demonstrate that,in instances where the punishment cost is prohibitively high,the cooperative strategies of the traditional pool punishment model may completely collapse.However,the model enriched with fine distribution manages to sustain a considerable number of cooperative strategies,thus highlighting its effectiveness in promoting and preserving cooperation,even in the face of substantial punishment cost.

Cooperative Distributed Beamforming Design for Multi-RIS Aided Cell-Free Systems

Cell-free systems significantly improve network capacity by enabling joint user service without cell boundaries,eliminating intercell interference.However,to satisfy further capacity demands,it leads to high-cost problems of both hardware and power consumption.In this paper,we investigate multiple reconfigurable intelligent surfaces(RISs)aided cell-free systems where RISs are introduced to improve spectrum efficiency in an energy-efficient way.To overcome the centralized high complexity and avoid frequent information exchanges,a cooperative distributed beamforming design is proposed to maximize the weighted sum-rate performance.In particular,the alternating optimization method is utilized with the distributed closed-form solution of active beamforming being derived locally at access points,and phase shifts are obtained centrally based on the Riemannian conjugate gradient(RCG)manifold method.Simulation results verify the effectiveness of the proposed design whose performance is comparable to the centralized scheme and show great superiority of the RISs-aided system over the conventional cellular and cell-free system.

Fully asynchronous distributed optimization with linear convergence over directed networks

We study distributed optimization problems over a directed network,where nodes aim to minimize the sum of local objective functions via directed communications with neighbors.Many algorithms are designed to solve it for synchronized or randomly activated implementation,which may create deadlocks in practice.In sharp contrast,we propose a fully asynchronous push-pull gradient(APPG) algorithm,where each node updates without waiting for any other node by using possibly delayed information from neighbors.Then,we construct two novel augmented networks to analyze asynchrony and delays,and quantify its convergence rate from the worst-case point of view.Particularly,all nodes of APPG converge to the same optimal solution at a linear rate of O(λ^(k)) if local functions have Lipschitz-continuous gradients and their sum satisfies the Polyak-?ojasiewicz condition(convexity is not required),where λ ∈(0,1) is explicitly given and the virtual counter k increases by one when any node updates.Finally,the advantage of APPG over the synchronous counterpart and its linear speedup efficiency are numerically validated via a logistic regression problem.

Distributed collaborative complete coverage path planning based on hybrid strategy

Collaborative coverage path planning(CCPP) refers to obtaining the shortest paths passing over all places except obstacles in a certain area or space. A multi-unmanned aerial vehicle(UAV) collaborative CCPP algorithm is proposed for the urban rescue search or military search in outdoor environment.Due to flexible control of small UAVs, it can be considered that all UAVs fly at the same altitude, that is, they perform search tasks on a two-dimensional plane. Based on the agents’ motion characteristics and environmental information, a mathematical model of CCPP problem is established. The minimum time for UAVs to complete the CCPP is the objective function, and complete coverage constraint, no-fly constraint, collision avoidance constraint, and communication constraint are considered. Four motion strategies and two communication strategies are designed. Then a distributed CCPP algorithm is designed based on hybrid strategies. Simulation results compared with patternbased genetic algorithm(PBGA) and random search method show that the proposed method has stronger real-time performance and better scalability and can complete the complete CCPP task more efficiently and stably.

Competitive and Cooperative-Based Strength Pareto Evolutionary Algorithm for Green Distributed Heterogeneous Flow Shop Scheduling

This work aims to resolve the distributed heterogeneous permutation flow shop scheduling problem(DHPFSP)with minimizing makespan and total energy consumption(TEC).To solve this NP-hard problem,this work proposed a competitive and cooperative-based strength Pareto evolutionary algorithm(CCSPEA)which contains the following features:1)An initialization based on three heuristic rules is developed to generate a population with great diversity and convergence.2)A comprehensive metric combining convergence and diversity metrics is used to better represent the heuristic information of a solution.3)A competitive selection is designed which divides the population into a winner and a loser swarms based on the comprehensive metric.4)A cooperative evolutionary schema is proposed for winner and loser swarms to accelerate the convergence of global search.5)Five local search strategies based on problem knowledge are designed to improve convergence.6)Aproblem-based energy-saving strategy is presented to reduce TEC.Finally,to evaluate the performance of CCSPEA,it is compared to four state-of-art and run on 22 instances based on the Taillard benchmark.The numerical experiment results demonstrate that 1)the proposed comprehensive metric can efficiently represent the heuristic information of each solution to help the later step divide the population.2)The global search based on the competitive and cooperative schema can accelerate loser solutions convergence and further improve the winner’s exploration.3)The problembased initialization,local search,and energy-saving strategies can efficiently reduce the makespan and TEC.4)The proposed CCSPEA is superior to the state-of-art for solving DHPFSP.

Distributed Model Predictive Control for Networked Plant-wide Systems With Neighborhood Cooperation

For large-scale networked plant-wide systems composed by physically(or geographically) divided subsystems, only limited information is available for local controllers on account of region and communication restrictions. Concerning the optimal control problem of such subsystems, a neighbor-based distributed model predictive control(NDMPC) strategy is presented to improve the global system performance. In this scheme, the performance index of local subsystems and that of its neighbors are minimized together in the determination of the optimal control input, which makes the local control decision also beneficial to its neighboring subsystems and further contributes to improving the convergence and control performance of overall system.The stability of the closed-loop system is proved. Moreover, the parameter designing method for distributed synthesis is provided.Finally, the simulation results illustrate the main characteristics and effectiveness of the proposed control scheme.

Distributed Asynchronous Learning for Multipath Data Transmission Based on P-DDQN

Adaptive packet scheduling can efficiently enhance the performance of multipath Data Transmission.However,realizing precise packet scheduling is challenging due to the nature of high dynamics and unpredictability of network link states.To this end,this paper proposes a distributed asynchronous deep reinforcement learning framework to intensify the dynamics and prediction of adaptive packet scheduling.Our framework contains two parts:local asynchronous packet scheduling and distributed cooperative control center.In local asynchronous packet scheduling,an asynchronous prioritized replay double deep Q-learning packets scheduling algorithm is proposed for dynamic adaptive packet scheduling learning,which makes a combination of prioritized replay double deep Q-learning network(P-DDQN)to make the fitting analysis.In distributed cooperative control center,a distributed scheduling learning and neural fitting acceleration algorithm to adaptively update neural network parameters of P-DDQN for more precise packet scheduling.Experimental results show that our solution has a better performance than Random weight algorithm and Round-Robin algorithm in throughput and loss ratio.Further,our solution has 1.32 times and 1.54 times better than Random weight algorithm and Round-Robin algorithm on the stability of multipath data transmission,respectively.

Distributed media cooperation for enhanced video communication

The author designed two algorithms for distributed cooperation among multiple video streams sharing common communication resources. The algorithms take advantage of an optimization framework that characterizes video packets such that joint resource allocation can be implemented not only over the packets of a single stream, but also across packets of different streams. The first algorithm enables collaboration among multiple video senders in an 802.11 CSMA/CA wireless network such that their joint performance is maximized. Via the algorithm, the users cooperatively establish transmission priorities based on the assigned characterizations of their video packets. The second technique allows for low-complexity joint bandwidth adaptation of multiple video streams at intermediate network nodes in the Internet in order to maximize the overall network performance. The author analyzes the advantages of the proposed algorithms over conventional solutions employed in such scenarios. It is shown that depending on system parameters such as available network data rate the proposed techniques can provide substantial gains in end-to-end performance.

STABILITY ANALYSIS AND COOPERATIVE CONTROL OF DISTRIBUTED MULTI-AGENT SYSTEM WITH SAMPLED COMMUNICATION

The cooperative control and stability analysis problems for the multi-agent system with sampled com- munication are investigated. Distributed state feedback controllers are adopted for the cooperation of networked agents. A theorem in the form of linear matrix inequalities（LMI） is derived to analyze the system stability. An- other theorem in the form of optimization problem subject to LMI constraints is proposed to design the controller, and then the algorithm is presented. The simulation results verify the validity and the effectiveness of the pro- posed approach.

A Whale Optimization Algorithm with Distributed Collaboration and Reverse Learning Ability

Due to the development of digital transformation,intelligent algorithms are getting more and more attention.The whale optimization algorithm(WOA)is one of swarm intelligence optimization algorithms and is widely used to solve practical engineering optimization problems.However,with the increased dimensions,higher requirements are put forward for algorithm performance.The double population whale optimization algorithm with distributed collaboration and reverse learning ability(DCRWOA)is proposed to solve the slow convergence speed and unstable search accuracy of the WOA algorithm in optimization problems.In the DCRWOA algorithm,the novel double population search strategy is constructed.Meanwhile,the reverse learning strategy is adopted in the population search process to help individuals quickly jump out of the non-ideal search area.Numerical experi-ments are carried out using standard test functions with different dimensions(10,50,100,200).The optimization case of shield construction parameters is also used to test the practical application performance of the proposed algo-rithm.The results show that the DCRWOA algorithm has higher optimization accuracy and stability,and the convergence speed is significantly improved.Therefore,the proposed DCRWOA algorithm provides a better method for solving practical optimization problems.

Distributed cooperative control of autonomous multi-agent UAV systems using smooth control

This paper addresses the cooperative control problem of multiple unmanned aerial vehicles(multi-UAV)systems.First,a new distributed consensus algorithm for second-order nonlinear multi-agent systems(MAS)is formulated under the leader-following approach.The algorithm provides smooth input signals to the agents’control channels,which avoids the chattering effect generated by the conventional sliding mode-based control protocols.Second,a new formation control scheme is developed by integrating smooth distributed consensus control protocols into the geometric pattern model to achieve three-dimensional formation tracking.The Lyapunov theory is used to prove the stability and convergence of both distributed consensus and formation controllers.The effectiveness of the proposed algorithms is demonstrated through simulation results.

Three-dimensional impact angle constrained distributed cooperative guidance law for anti-ship missiles

This paper investigates the problem of distributed cooperative guidance law design for multiple anti-ship missiles in the three-dimensional(3-D)space hitting simultaneously the same target with considering the desired terminal impact angle constraint.To address this issue,the problem formulation including 3-D nonlinear mathematical model description,and communication topology are built firstly.Then the consensus variable is constructed using the available information and can reach consensus under the proposed acceleration command along the line-of-sight(LOS)which satisfies the impact time constraint.However,the normal accelerations are designed to guarantee the convergence of the LOS angular rate.Furthermore,consider the terminal impact angle constraints,a nonsingular terminal sliding mode(NTSM)control is introduced,and a finite time convergent control law of normal acceleration is proposed.The convergence of the proposed guidance law is proved by using the second Lyapunov stability method,and numerical simulations are also conducted to verify its effectiveness.The results indicate that the proposed cooperative guidance law can regulate the impact time error and impact angle error in finite time if the connecting time of the communication topology is longer than the required convergent time.

A DISTRIBUTED COOPERATIVE MULTICAST SCHEDULING STRATEGY IN IEEE 802.11 NETWORKS

In wireless multicast, some users may fail to receive data as a result of bad channel con-ditions. The throughput of traditional multicast strategy is constrained by the node with the worst channel condition. In this paper, we propose a distributed cooperative multicast scheduling strategy, in which, every user which has successfully received data can decide whether to retransmit data by sensing MNACK packets. The proposed scheme can achieve good throughput by exploiting multi-channel diversity across multiple users' cooperation. In addition, the constraint of power consumption is also considered. Simulation results show that our scheme can provide good throughput and fairness per-formance with the constraint of average system power consumption.

Improved scheme with cooperative diversity based on distributed space-time block coding

An improved scheme with cooperative diversity based on distributed space-time block coding （WCD- DSTBC） is proposed, which effectively achieves diversity gains and improves the performance of the system by sharing some single-antenna users＇ antennas to form a virtual antenna array and combining with distributed spacetime block coding （DSTBC） mode. Then the relation between the system BER and the interuser BER for WCDDSTBC scheme is theoretically derived and the closed-form expression of BER for WCD-DSTBC system is obtained. The simulation results show that the proposed WCD-DSTBC scheme achieves distinct gains over the non-cooperative multi-carrier CDMA （MC-CDMA） system. When system BER is le-3 and interuser BER is le-3, about 2.5 dB gain can be gotten. When interuser channel state information （CSI） outgoes the users＇ individual CSI, about 3 dB gain is also achieved.

INTERFERENCE MITIGATION FOR ASYNCHRONOUS COOPERATIVE STBC-OFDM SYSTEMS

This paper addresses the problem of interference mitigation in cooperative Space Time Block Coded Orthogonal Frequency Division Multiplexing (STBC-OFDM) systems in the presence of asyn-chronism. This scheme first preprocesses the received ST codewords to convert the equivalent fading matrix into a suboptimal ordering upper triangular form based on low complexity permutation QR decomposition, and then suppresses the InterCarrier Interference (ICI) and InterSymbol Interference (ISI) by exploiting Successive Interference Cancellation (SIC) technique. Simulation results show that the performance of the proposed algorithm slightly outmatches or asymptotically approaches to that of the existing Minimum Mean Square Error (MMSE) detector depending on the magnitude of the Carrier Frequency Offsets (CFOs) but with less complexity.

Optimal Set of Multiple Relays and Distributed Self-Selection in Cooperative Networks

In this paper we derive analytically the optimal set of relays for the maximal destination signal-to-noise ratio (SNR) in a two-hop amplify-and-forward cooperative network with frequency-selective fading channels. Simple rules are derived to determine the optimal relays from all available candidates. Our results show that a node either participates in relaying with full power or does not participate in relaying at all, and that a node is a valid relay if and only if its SNR is higher than the optimal destination SNR. In addition, we develop a simple distributed algorithm for each node to determine whether participating in relaying by comparing its own SNR with the broadcasted destination SNR. This algorithm has extremely low overhead, and is shown to converge to the optimal solution fast and exactly within a finite number of iterations. The extremely high efficiency makes it especially suitable to time-varying mobile networks.

Networked Cooperative Distributed Model Predictive Control Based on State Observer

Although distributed model predictive control has caused significant attention and received many good results, the results are mostly under the assumption that the system states can be observed. However, the states are difficult to be observed in practice. In this paper, a novel distributed model predictive control is proposed based on state observer for a kind of linear discrete-time systems where states are not measured. Firstly, an output feedback control law is designed based on Lyapunov function and state observer. And the stability domain is described. Furthermore, the stability domain as a terminal constraint is added into the constraint conditions of the algorithm to make systems stable outside the stability domain. The simulation results show the effectiveness of the proposed method.

Asynchronous cooperative transmission scheme using linear dispersion code

The cooperative diversity schemes can effectively create a virtual antenna array for path fading combating multiin wireless channels. However, a lot of cooperative diversity schemes require perfect synchronization which is, in practice, difficult and even impossible to be realized. In this paper, we propose an asynchronous cooperative diversity scheme based on the linear dispersion code （LDC）. By adding the zero padding （ZP） between linear dispersion codewords, our scheme mitigates the effect of asynchronism effectively. The length of ZP is decided by relative timing errors between different relays. Besides, an easy decoding method of our scheme is given in this paper by restructuring the stacked channel matrix.

Triggered Asynchronous Cooperative Spectrum Sensing Scheme Based on Dempster-Shafer Theory

Aiming at solving the problems such as time consuming and application limiting presented in the existing synchronous cooperative spectrum sensing schemes,a triggered asynchronous scheme based on Dempster-Shafer(D-S) theory was proposed.Sensing asynchronously,each cognitive user calculated the confidence measure functions with double threshold spectrum sensing method.When the useful report was received by the fusion center,a fusion process would be triggered.Then the sensing results were fused together based on D-S theory.The analysis and simulation results show that the proposed scheme can improve the spectrum sensing efficiency and reduce the calculation amount of the fusion center compared with the existing schemes.

Distributed Control for <i>n</i>×<i>n</i>Cooperative Systems Governed by Hyperbolic Operator of Infinite Order

In this study, a distributed optimal control problem for n × n cooperative hyperbolic systems with infinite order operators and Dirichlet conditions are considered. The existence and uniqueness of the state of these systems are proved. The necessary and sufficient conditions for optimality of distributed control with constraints are found, and the set of equations and inequalities that defining the optimal control of these systems is also obtained. Finally, some examples for the control problem without constraints are given.