Path planning in 3D geometry space is used to find an optimal path in the restricted environment, according to a certain evaluation criteria. To solve the problem of long searching time and slow solving speed in 3D pa...Path planning in 3D geometry space is used to find an optimal path in the restricted environment, according to a certain evaluation criteria. To solve the problem of long searching time and slow solving speed in 3D path planning, a modified ant colony optimization is proposed in this paper. Firstly, the grid method for environment modeling is adopted. Heuristic information is connected with the planning space. A semi-iterative global pheromone update mechanism is proposed. Secondly, the optimal ants mutate the paths to improve the diversity of the algorithm after a defined iterative number. Thirdly, co-evolutionary algorithm is used. Finally, the simulation result shows the effectiveness of the proposed algorithm in solving the problem of 3D pipe path planning.展开更多
Emergency management requires efcient evacuation planning and the delivery of rescue supplies within dynamic road networks disrupted by ongoing disasters.Two critical challenges arise:(1)determining appropriate origin...Emergency management requires efcient evacuation planning and the delivery of rescue supplies within dynamic road networks disrupted by ongoing disasters.Two critical challenges arise:(1)determining appropriate origin-destination(OD)assignments;and(2)identifying optimal paths among multiple OD pairs in real time.However,traditional static path optimization(SPO)and dynamic path optimization(DPO)often fall short in adapting to rapidly evolving conditions,risking failure in emergency response.To address these limitations,we proposed a novel method by modifying the co-evolutionary path optimization(CEPO)based on the ripple spreading algorithm(RSA),which can simultaneously determine optimal OD pairs and corresponding paths in a single run,even under dynamic disaster environment.The efectiveness and advantages of the method are verifed by comprehensive experiments.展开更多
基金Supported by National Natural Science Foundation of China (50875165)
文摘Path planning in 3D geometry space is used to find an optimal path in the restricted environment, according to a certain evaluation criteria. To solve the problem of long searching time and slow solving speed in 3D path planning, a modified ant colony optimization is proposed in this paper. Firstly, the grid method for environment modeling is adopted. Heuristic information is connected with the planning space. A semi-iterative global pheromone update mechanism is proposed. Secondly, the optimal ants mutate the paths to improve the diversity of the algorithm after a defined iterative number. Thirdly, co-evolutionary algorithm is used. Finally, the simulation result shows the effectiveness of the proposed algorithm in solving the problem of 3D pipe path planning.
基金supported by the Civil Aviation Safety Capacity Building Project of China(HA202511).
文摘Emergency management requires efcient evacuation planning and the delivery of rescue supplies within dynamic road networks disrupted by ongoing disasters.Two critical challenges arise:(1)determining appropriate origin-destination(OD)assignments;and(2)identifying optimal paths among multiple OD pairs in real time.However,traditional static path optimization(SPO)and dynamic path optimization(DPO)often fall short in adapting to rapidly evolving conditions,risking failure in emergency response.To address these limitations,we proposed a novel method by modifying the co-evolutionary path optimization(CEPO)based on the ripple spreading algorithm(RSA),which can simultaneously determine optimal OD pairs and corresponding paths in a single run,even under dynamic disaster environment.The efectiveness and advantages of the method are verifed by comprehensive experiments.
