Shuffled frog leaping algorithm with non-dominated sorting for dynamic weapon-target assignment

The dynamic weapon target assignment(DWTA)problem is of great significance in modern air combat.However,DWTA is a highly complex constrained multi-objective combinatorial optimization problem.An improved elitist non-dominated sorting genetic algorithm-II(NSGA-II)called the non-dominated shuffled frog leaping algorithm(NSFLA)is proposed to maximize damage to enemy targets and minimize the self-threat in air combat constraints.In NSFLA,the shuffled frog leaping algorithm(SFLA)is introduced to NSGA-II to replace the inside evolutionary scheme of the genetic algorithm(GA),displaying low optimization speed and heterogeneous space search defects.Two improvements have also been raised to promote the internal optimization performance of SFLA.Firstly,the local evolution scheme,a novel crossover mechanism,ensures that each individual participates in updating instead of only the worst ones,which can expand the diversity of the population.Secondly,a discrete adaptive mutation algorithm based on the function change rate is applied to balance the global and local search.Finally,the scheme is verified in various air combat scenarios.The results show that the proposed NSFLA has apparent advantages in solution quality and efficiency,especially in many aircraft and the dynamic air combat environment.

Bus frequency optimization in a large-scale multi-modal transportation system:integrating 3D-MFD and dynamic traffic assignment

A properly designed public transport system is expected to improve traffic efficiency.A high-frequency bus service would decrease the waiting time for passengers,but the interaction between buses and cars might result in more serious congestion.On the other hand,a low-frequency bus service would increase the waiting time for passengers and would not reduce the use of private cars.It is important to strike a balance between high and low frequencies in order to minimize the total delays for all road users.It is critical to formulate the impacts of bus frequency on congestion dynamics and mode choices.However,as far as the authors know,most proposed bus frequency optimization formulations are based on static demand and the Bureau of Public Roads function,and do not properly consider the congestion dynamics and their impacts on mode choices.To fill this gap,this paper proposes a bi-level optimization model.A three-dimensional Macroscopic Fundamental Diagram based modeling approach is developed to capture the bi-modal congestion dynamics.A variational inequality model for the user equilibrium in mode choices is presented and solved using a double projection algorithm.A surrogate model-based algorithm is used to solve the bi-level programming problem.

Survey of the research on dynamic weapon-target assignment problem

The basic concepts and models of weapon-target assignment （WTA） are introduced and the mathematical nature of the WTA models is also analyzed. A systematic survey of research on WTA problem is provided. The present research on WTA is focused on models and algorithms. In the research on models of WTA, the static WTA models are mainly studied and the dynamic WTA models are not fully studied in deed. In the research on algorithms of WTA, the intelligent algorithms are often used to solve the WTA problem. The small scale of static WTA problems has been solved very well, however, the large scale of dynamic WTA problems has not been solved effectively so far. Finally, the characteristics of dynamic WTA are analyzed and directions for the future research on dynamic WTA are discussed.

Improved MOEA/D for Dynamic Weapon-Target Assignment Problem

Conducting reasonable weapon-target assignment( WTA) with near real time can bring the maximum awards with minimum costs which are especially significant in the modern war. A framework of dynamic WTA( DWTA) model based on a series of staged static WTA( SWTA) models is established where dynamic factors including time window of target and time window of weapon are considered in the staged SWTA model. Then,a hybrid algorithm for the staged SWTA named Decomposition-Based Dynamic Weapon-target Assignment( DDWTA) is proposed which is based on the framework of multi-objective evolutionary algorithm based on decomposition( MOEA / D) with two major improvements: one is the coding based on constraint of resource to generate the feasible solutions, and the other is the tabu search strategy to speed up the convergence.Comparative experiments prove that the proposed algorithm is capable of obtaining a well-converged and well diversified set of solutions on a problem instance and meets the time demand in the battlefield environment.

Research on traffic congestion mechanism and countermeasures based on dynamic traffic assignment

Traffic congestion is widely distributed around a network. Generally, to analyze traffic congestion, static traffic capacity is adopted. But dynamic characteristics must be studied because congestion is a dynamic process. A Dynamic Traffic Assignment modeling fundamental combined with an urban congestion analysis method is studied in this paper. Three methods are based on congestion analysis, and the stochastic user optimal DTA models are especially considered. Correspondingly, a dynamic system optimal model is suggested for responding congestion countermeasures and an ideal user optimal model for predicted congestion countermeasure respectively.

Dynamic routing and wavelength assignment algorithm of optical satellite networks based on cross-layer design

In order to overcome the adverse effects of Doppler wavelength shift on data transmission in the optical satellite networks,a dynamic routing and wavelength assignment algorithm based on crosslayer design( CL-DRWA) is introduced which can improve robustness of the network. Above all,a cross-layer optimization model is designed,which considers transmission delay and wavelength-continuity constraint,as well as Doppler wavelength shift. Then CL-DRWA is applied to solve this model,resulting in finding an optimal light path satisfying the above constraints for every connection request. In CL-DRWA,Bellman-Ford method is used to find an optimal route and a distributed relative capacity loss method is implemented to get an optimal wavelength assignment result on the optimal route. Moreover,compared with the dynamic routing and wavelength assignment algorithm based on minimum delay strategy( MD-DRWA),CL-DRWA can make an improvement of 5. 3% on the communication success probability. Meanwhile,CL-DRWA can meet the requirement of transmission delay for real-time services.

Assignment Theory and Application of Intelligent Traffic Systems

Assignment theory of intelligent traffic systems (ITS) is a new information management system of vehicle in recent developments. In this paper, the basic concepts of the theory and three representative studies, that is, (1) simulation based approach, (2)optimal control theory approach, and (3) optimization approach are introduced. Their advantages and drawbacks are analyzed and expounded. Also introduced are two latest techniques of assignment theory of ITS. Finally, a dynamic assignment model of a traffic transport network is proposed.

Cooperative Channel Assignment for VANETs Based on Dual Reinforcement Learning

Dynamic channel assignment(DCA)is significant for extending vehicular ad hoc network(VANET)capacity and mitigating congestion.However,the un-known global state information and the lack of centralized control make channel assignment performances a challenging task in a distributed vehicular direct communication scenario.In our preliminary field test for communication under V2X scenario,we find that the existing DCA technology cannot fully meet the communication performance requirements of VANET.In order to improve the communication performance,we firstly demonstrate the feasibility and potential of reinforcement learning(RL)method in joint channel selection decision and access fallback adaptation design in this paper.Besides,a dual reinforcement learning(DRL)-based cooperative DCA(DRL-CDCA)mechanism is proposed.Specifically,DRL-CDCA jointly optimizes the decision-making behaviors of both the channel selection and back-off adaptation based on a multi-agent dual reinforcement learning framework.Besides,nodes locally share and incorporate their individual rewards after each communication to achieve regional consistency optimization.Simulation results show that the proposed DRL-CDCA can better reduce the one-hop packet delay,improve the packet delivery ratio on average when compared with two other existing mechanisms.

Bifurcation control and eigenstructure assignment in continuous solution polymerization of vinyl acetate

The major difficulty in achieving good performance of industrial polymerization reactors lies in the lack of understanding of their nonlinear dynamics and the lack of well-developed techniques for the control of nonlinear processes, which are usually accompanied with bifurcation phenomenon. This work aims at investigating the nonlinear behavior of the parameterized nonlinear system of vinyl acetate polymerization and further modifying the bifurcation characteristics of this process via a washout filter-aid controller, with all the original steady state equilibria preserved. Advantages and possible extensions of the proposed methodology are discussed to provide scientific guide for further controller design and operation improvement.

The Dynamic Channel Allocation Scheme Based on Stratification and Simulated Annealing Method

This paper proposes a dynamic channel allocation scheme based on cognitive radio （CR）. Firstly, the channel probing based on MMSE criterion is implemented, with which the probability distribution of channels in use by the primary user is given. Next, take the distances between the CR users and the primary user as basis to stratify the CR users, among the layers; the simulated annealing （SA） algorithm is used to implement the channel assigmnent. This algorithm differs from the well-known 0-1 matrix based allocation scheme, and keeps a good tradeoff between complexity, capacity as well as the fairness problems. The simulation results show that this algorithm can improve the allocation efficiency effectively.

Evolutionary decision-makings for the dynamic weapon-target assignment problem

The dynamic weapon-target assignment （DWTA） problem is an important issue in the field of military command and control. An asset-based DWTA optimization model was proposed with four kinds of constraints considered, including capability constraints, strategy constraints, resource constraints and engagement feasibility constraints. A general ＂virtual＂ representation of decisions was presented to facilitate the generation of feasible decisions. The representation is in essence the permutation of all assignment pairs. A construction procedure converts the permutations into real feasible decisions. In order to solve this problem, three evolutionary decision-making algorithms, including a genetic algorithm and two memetic algorithms, were developed. Experimental results show that the memetic algorithm based on greedy local search can generate obviously better DWTA decisions, especially for large-scale problems, than the genetic algorithm and the memetic algorithm based on steepest local search.

Dynamic Multi-Wavelength Assignment with Link Aggregation on SLAMNet Test System

This paper presents the implementation and performance of the best-effort multi-wavelength assignment with link aggregation on the SLAMNet (Statistical Lambda Multiplexing Network) test system.

Integration control for traffic corridors considering guidance information

The problem of designing integration traffic strategies for traffic corridors with the use of ramp metering, speed limit, and route guidance is considered in this paper. As an improvement to the previous work, the presented approach has the following five features： 1） modeling traffic flow to analyze traffic characteristics under the influence of variable speed limit, on-ramp metering and guidance information; 2） building a hierarchy model to realize the integration design of traffic control and route guidance in traffic corridors; 3） devising a multi-class analytical dynamic traffic assignment （DTA） model for traffic corridors, where not only the route choice process will be different for each user-class, but also the traffic flow operations are user-class specific because the travel time characteristic for each user-class is considered; 4） predicting route choice probabilities adaptively with real-time traffic conditions and route choice behaviors corresponding to variant users, rather than assuming as pre-determined; and 5） suggesting a numerical solution algorithm of the hierarchy model presented in this paper based on the modified algorithm of iterative optimization assignment （IOA）. Preliminary numerical test demonstrates the potential of the developed model and algorithm for integration corridor control.

Q-learning for dynamic channel assignment in cognitive wireless local area network with fibre-connected distributed antennas

Cognitive wireless local area network with fibre-connected distributed antennas （CWLAN-FDA） is a promising and efficient architecture that combines radio over fiber, cognitive radio and distributed antenna technologies to provide high speed/high capacity wireless access at a reasonable cost. In this paper, a Q-learning approach is applied to implement dynamic channel assignment （DCA） in CWLAN-FDA. The cognitive access points （CAPs） select and assign the best channels among the industrial, scientific, and medical （ISM） band for data packet transmission, given that the objective is to minimize external interference and acquire better network-wide performance. The Q-learning method avoids solving complex optimization problem while being able to explore the states of a CWLAN-FDA system during normal operations. Simulation results reveal that the proposed strategy is effective in reducing outage probability and improving network throughput.

Route choice models based on altering OD

The condition and physical sense of actual dynamic user optimum are explained by analyzing a simple road network route choice. To match the practical application requirements, assignment network and simulation network are classified account for varying flowing loading. Instantaneous dynamic user optimum model should be applied to the former and actual dynamic user optimum model the latter respectively. The two model’s feasibility is studied as well. Considering the application in ATMS, the model is mainly used to analyze the altering OD problem. Moreover, it adds the method of route adapting into the object function selection to appraise elastic trip strategy and set up real means of route inducement.

Cooperative channel assignment for VANETs based on multiagent reinforcement learning

Dynamic channel assignment(DCA)plays a key role in extending vehicular ad-hoc network capacity and mitigating congestion.However,channel assignment under vehicular direct communication scenarios faces mutual influence of large-scale nodes,the lack of centralized coordination,unknown global state information,and other challenges.To solve this problem,a multiagent reinforcement learning(RL)based cooperative DCA(RLCDCA)mechanism is proposed.Specifically,each vehicular node can successfully learn the proper strategies of channel selection and backoff adaptation from the real-time channel state information(CSI)using two cooperative RL models.In addition,neural networks are constructed as nonlinear Q-function approximators,which facilitates the mapping of the continuously sensed input to the mixed policy output.Nodes are driven to locally share and incorporate their individual rewards such that they can optimize their policies in a distributed collaborative manner.Simulation results show that the proposed multiagent RL-CDCA can better reduce the one-hop packet delay by no less than 73.73%,improve the packet delivery ratio by no less than 12.66%on average in a highly dense situation,and improve the fairness of the global network resource allocation.