Xuejun Zhang

A hybrid distributed-centralized conflict resolution approach for multi-aircraft based on cooperative co-evolutionary

Conflict resolution (CR) plays a crucial role in safe air traffic management (ATM). In this paper, we propose a new hybrid distributed-centralized tactical CR approach based on cooperative co-evolutionary named the CCDG (cooperative co-evolutionary with dynamic grouping) strategy to overcome the drawbacks of the current two types of approaches, the totally centralized approach and distributed approach. Firstly, aircraft are divided into several sub-groups based on their interdependence. Besides, a dynamic grouping strategy is proposed to group the aircraft to better deal with the tight coupling among them. The sub-groups are adjusted dynamically as new conflicts appear after each iteration. Secondly, a fast genetic algorithm (GA) is used by each sub-group to optimize the paths of the aircraft simultaneously. Thirdly, the aircraft’s optimal paths are obtained through cooperation among different sub-groups based on cooperative co-evolutionary (CC). An experimental study on two illustrative scenarios is conducted to compare the CCDG method with some other existing approaches and it is shown that CCDG which can get the optimal solution effectively and efficiently in near real time, outperforms most of the existing approaches including Stratway, the fast GA, a general evolutionary path planner, as well as three well-known cooperative co-evolution algorithms.

An efficient routing strategy on spatial scale-free networks

Traffic dynamics has drawn much more attention recently, but most current research barely considers the space factor, which is of critical importance in many real traffic systems. In this paper, we focus our research on traffic dynamics of a spatial scale-free network with the restriction of bandwidth proportional to link Euclidean distance, and a new routing strategy is proposed with consideration of both Euclidean distance and betweenness centralities (BC) of edges. It is found that compared with the shortest distance path (SDP) strategy and the minimum betweenness centralities (MBC) of links strategy, our strategy under some parameters can effectively balance the traffic load and avoid excessive traveling distance which can improve the spatial network capacity and some system behaviors reflecting transportation efficiency, such as average packets traveling time, average packets waiting time and system throughput, traffic load and so on. Besides, though the restriction of bandwidth can trigger congestion, the proposed routing strategy always has the best performance no matter what bandwidth becomes. These results can provide insights for research on real networked traffic systems.

4D-Trajectory Conflict Resolution Using Cooperative Coevolution

Conflict resolution becomes a worldwide urgent problem to guarantee the airspace safety. The existing approaches are mostly short-term or middle-term which obtain solutions by local adjustment. 4D-Trajectory conflict resolution (4DTCR), as a long-term method, can give better solutions to all flights in a global view. 4DTCR involved with China air route network and thousands of flight plans is a large and complex problem which is hard to be solved by classical approaches. In this paper, the cooperative coevolution (CC) algorithm with random grouping strategy is presented for its advantage in dealing with large and complex problem. Moreover, a fast Genetic Algorithm (GA) is designed for each subcomponent optimization which is effective and efficient to obtain optimal solution. Experimental studies are conducted to compare it to the genetic algorithm in previous approach and CC algorithm with classic grouping strategy. The results show that our algorithm has a better performance.

A strategic conflict avoidance approach based on cooperative coevolutionary with the dynamic grouping strategy

Conflict avoidance plays a crucial role in guaranteeing the safety and efficiency of the air traffic management system. Recently, the strategic conflict avoidance (SCA) problem has attracted more and more attention. Taking into consideration the large-scale flight planning in a global view, SCA can be formulated as a large-scale combinatorial optimisation problem with complex constraints and tight couplings between variables, which is difficult to solve. In this paper, an SCA approach based on the cooperative coevolution algorithm combined with a new decomposition strategy is proposed to prevent the premature convergence and improve the search capability. The flights are divided into several groups using the new grouping strategy, referred to as the dynamic grouping strategy, which takes full advantage of the prior knowledge of the problem to better deal with the tight couplings among flights through maximising the chance of putting flights with conflicts in the same group, compared with existing grouping strategies. Then, a tuned genetic algorithm (GA) is applied to different groups simultaneously to resolve conflicts. Finally, the high-quality solutions are obtained through cooperation between different groups based on cooperative coevolution. Simulation results using real flight data from the China air route network and daily flight plans demonstrate that the proposed algorithm can reduce the number of conflicts and the average delay effectively, outperforming existing approaches including GAs, the memetic algorithm, and the cooperative coevolution algorithms with different well-known grouping strategies.

A methodology for designing transition route network between en-route airspace and terminal areas

A methodology is presented, in this paper, to design the transition network around the terminal areas (TMAs). The methodology consists of two parts: the multi-objective model and the algorithm. Our model is based on the definition of the optimal transition network, which refers to a tradeoff among the economy, the safety and the structure simplicity of the network. The economy, namely the cost of the network is denoted by the mileage of all the aircraft, the safety is expressed by the attribute-the conflicts of the network, and the structure simplicity of the network is reflected by the balance of the aircraft flow entering / departing TMAs in each transition routes. Then an evolutionary optimization algorithm, non-dominated sorting genetic algorithm 2 (NSGA2), is applied to solve the model and provide the optimal transition network structure. The model is validated by the data from the transition network of Beijing TMA, which demonstrates the superiority of the methodology by the comparison of the transition network performance with the current one.

An improved genetic algorithm with dynamic topology

The genetic algorithm (GA) is a nature-inspired evolutionary algorithm to find optima in search space via the interaction of individuals. Recently, researchers demonstrated that the interaction topology plays an important role in information exchange among individuals of evolutionary algorithm. In this paper, we investigate the effect of different network topologies adopted to represent the interaction structures. It is found that GA with a high-density topology ends up more likely with an unsatisfactory solution, contrarily, a low-density topology can impede convergence. Consequently, we propose an improved GA with dynamic topology, named DT-GA, in which the topology structure varies dynamically along with the fitness evolution. Several experiments executed with 15 well-known test functions have illustrated that DT-GA outperforms other test GAs for making a balance of convergence speed and optimum quality. Our work may have implications in the combination of complex networks and computational intelligence.

An Airway Network Flow Assignment Approach Based on an Efficient Multiobjective Optimization Framework

Considering reducing the airspace congestion and the flight delay simultaneously, this paper formulates the airway network flow assignment (ANFA) problem as a multiobjective optimization model and presents a new multiobjective optimization framework to solve it. Firstly, an effective multi-island parallel evolution algorithm with multiple evolution populations is employed to improve the optimization capability. Secondly, the nondominated sorting genetic algorithm II is applied for each population. In addition, a cooperative coevolution algorithm is adapted to divide the ANFA problem into several low-dimensional biobjective optimization problems which are easier to deal with. Finally, in order to maintain the diversity of solutions and to avoid prematurity, a dynamic adjustment operator based on solution congestion degree is specifically designed for the ANFA problem. Simulation results using the real traffic data from China air route network and daily flight plans demonstrate that the proposed approach can improve the solution quality effectively, showing superiority to the existing approaches such as the multiobjective genetic algorithm, the well-known multiobjective evolutionary algorithm based on decomposition, and a cooperative coevolution multiobjective algorithm as well as other parallel evolution algorithms with different migration topology.

A large-scale multi-objective flights conflict avoidance approach supporting 4D trajectory operation

Recently, the long-term conflict avoidance approaches based on large-scale flights scheduling have attracted much attention due to their ability to provide solutions from a global point of view. However, current approaches which focus only on a single objective with the aim of minimizing the total delay and the number of conflicts, cannot provide controllers with variety of optional solutions, representing different tradeoffs. Furthermore, the flight track error is often overlooked in the current research. Therefore, in order to make the model more realistic, in this paper, we formulate the long-term conflict avoidance problem as a multi-objective optimization problem, which minimizes the total delay and reduces the number of conflicts simultaneously. As a complex air route network needs to accommodate thousands of flights, the problem is a large-scale combinatorial optimization problem with tightly coupled variables, which make the problem difficult to deal with. Hence, in order to further improve the search capability of the solution algorithm, a cooperative co-evolution (CC) algorithm is also introduced to divide the complex problem into several low dimensional sub-problems which are easier to solve. Moreover, a dynamic grouping strategy based on the conflict detection is proposed to improve the optimization efficiency and to avoid premature convergence. The well-known multi-objective evolutionary algorithm based on decomposition (MOEA/D) is then employed to tackle each sub-problem. Computational results using real traffic data from the Chinese air route network demonstrate that the proposed approach obtained better non-dominated solutions in a more effective manner than the existing approaches, including the multi-objective genetic algorithm (MOGA), NSGAII, and MOEA/D. The results also show that our approach provided satisfactory solutions for controllers from a practical point of view.

Cascading failure in scale-free networks with tunable clustering

Cascading failure is ubiquitous in many networked infrastructure systems, such as power grids, Internet and air transportation systems. In this paper, we extend the cascading failure model to a scale-free network with tunable clustering and focus on the effect of clustering coefficient on system robustness. It is found that the network robustness undergoes a nonmonotonic transition with the increment of clustering coefficient: both highly and lowly clustered networks are fragile under the intentional attack, and the network with moderate clustering coefficient can better resist the spread of cascading. We then provide an extensive explanation for this constructive phenomenon via the microscopic point of view and quantitative analysis. Our work can be useful to the design and optimization of infrastructure systems.

Hybrid routing for interconnected BA scale-free networks

Interconnections between networks make the traffic condition in interconnected networks more complicated than that in an isolated network. They make the load and capacity of nodes mismatch and restrict the traffic performance accordingly. To improve the performance, in this paper, we propose a hybrid routing strategy, which distinguishes the traffic within each individual network and the traffic across multiple networks and uses different routing rules for these two types of traffic. Simulation results show that this routing strategy can achieve better traffic performance than traditional strategies when networks are coupled by a small number of interconnected links, which is the case in most of real-world interconnected networks. Therefore, the proposed hybrid routing strategy can find applications in the planning and optimization of practical interconnected networks.