Optimization of Air Route Network Nodes to Avoid ″Three Areas″ Based on An Adaptive Ant Colony Algorithm

Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective function,and an air route network node(ARNN)optimization model was developed to circumvent the restrictions imposed by″three areas″,also known as prohibited areas,restricted areas,and dangerous areas(PRDs),by creating agrid environment.And finally the objective function was solved by means of an adaptive ant colony algorithm(AACA).The A593,A470,B221,and G204 air routes in the busy ZSHA flight information region,where the airspace includes areas with different levels of PRDs,were taken as an example.Based on current flight patterns,a layout optimization of the ARNN was computed using this model and algorithm and successfully avoided PRDs.The optimized result reduced the total length of routes by 2.14% and the total cost by 9.875%.

The Model Identification for Small Unmanned Aerial Rotorcraft Based on Adaptive Ant Colony Algorithm

This paper proposes a model identification method to get high performance dynamic model of a small unmanned aerial rotorcraft. With the analysis of flight characteristics, a linear dynamic model is constructed by the small perturbation theory. Using the micro guidance navigation and control module, the system can record the control signals of servos, the state infor- mation of attitude and velocity information in sequence. After the data preprocessing, an adaptive ant colony algorithm is proposed to get optimal parameters of the dynamic model. With the adaptive adjustment of the pheromone in the selection process, the proposed model identification method can escape from local minima traps and get the optimal solution quickly. Performance analysis and experiments are conducted to validate the effectiveness of the identified dynamic model. Compared with real flight data, the identified model generated by the proposed method has a better performance than the model generated by the adaptive genetic algorithm. Based on the identified dynamic model, the small unmanned aerial rotorcraft can generate suitable control parameters to realize stable hovering, turning, and straight flight.

Unequal clustering algorithm for WSN based on fuzzy logic and improved ACO

This paper proposes a novel energy efficient unequal clustering algorithm for large scale wireless sensor network （WSN） which aims to balance the node power consumption and prolong the network lifetime as long as possible. Our approach focuses on energy efficient unequal clustering scheme and inter-cluster routing protocol. On the one hand, considering each node＇s local information such as energy level, distance to base station and local density, we use fuzzy logic system to determine one node＇s chance of becoming cluster head and hand, adaptive max-min ant colony optimization is used to estimate the corresponding competence radius. On the other construct energy-aware inter-cluster routing between cluster heads and base station （BS）, which balances the energy consumption of cluster heads and alleviates the hot spots problem that occurs in multi-hop WSN routing protocol to a large extent. The confirmation experiment results have indicated the proposed clustering algorithm has more superior performance than other methods such as low energy adaptive clustering hierarchy （LEACH） and energy efficient unequal clustering （EEUC）.